Forensic Investigation Capabilities of Microsoft Azure: A Comprehensive Analysis and Its Significance in Advancing Cloud Cyber Forensics

Abstract

This article delves into Microsoft Azure’s cyber forensic capabilities, focusing on the unique challenges in cloud security incident investigation. Cloud services are growing in popularity, and Azure’s shared responsibility model, multi-tenant nature, and dynamically scalable resources offer unique advantages and complexities for digital forensics. These factors complicate forensic evidence collection, preservation, and analysis. Data collection, logging, and virtual machine analysis are covered, considering physical infrastructure restrictions and cloud data transience. It evaluates Azure-native and third-party forensic tools and recommends methods that ensure effective investigations while adhering to legal and regulatory standards. It also describes how AI and machine learning automate data analysis in forensic investigations, improving speed and accuracy. This integration advances cyber forensic methods and sets new standards for future innovations. Unified Audit Logs (UALs) in Azure are examined, focusing on how Azure Data Explorer and Kusto Query Language (KQL) can effectively parse and query large datasets and unstructured data to detect sophisticated cyber threats. The findings provide a framework for other organizations to improve forensic analysis, advancing cloud cyber forensics while bridging theoretical practices and practical applications, enhancing organizations’ ability to combat increasingly sophisticated cybercrime.

1. Introduction

Cyber forensics is a crucial and rapidly advancing field that deals with the intricacies of investigating cybercrime in the modern era. In the face of increasingly advanced and widespread cyber threats, the importance of cyber forensics in detecting, safeguarding, examining, and presenting digital evidence has reached unprecedented levels. Cyber forensics utilizes scientific methodologies and sophisticated technological instruments to investigate various incidents, including cyber-attacks, data breaches, online fraud, and cyberbullying. This field is vital for law enforcement agencies, private sector security teams, and the judicial system, as it not only provides the essential evidence to convict criminals but also formulates strategies to prevent future occurrences, instilling a sense of hope and optimism in the fight against cybercrime.

The increase in cyber threats and complex attack methods has brought attention to significant difficulties in cyber forensics. Forensic investigators must keep up with the latest tools and methodologies to stay ahead of cybercriminals who constantly change their techniques. Integrating artificial intelligence and machine learning is necessary to enhance forensic capabilities and address modern cyber threats’ complexities, as traditional forensic methods are often inadequate. These sophisticated technologies can automate the examination of vast amounts of data, detect patterns, and offer more profound insights into cyber incidents, ultimately resulting in enhanced accuracy and efficiency in investigations.

Cloud forensics involves addressing challenges about data ownership, jurisdiction, and the ever-changing nature of cloud storage.

This paper examines the present state of cyber forensics, highlighting the significance of ongoing improvements in forensic tools and methodologies to stay updated with the constantly evolving cyber threat landscape. This paper explores incorporating artificial intelligence to improve forensic capabilities, simplify the investigative process, and tackle the urgent requirements for precision and effectiveness. This paper also examines the distinct difficulties various digital environments present and the need for specialized forensic methodologies to tackle these challenges effectively.

Moreover, this paper emphasizes the crucial requirement for standardized protocols and comprehensive frameworks in cyber forensics. The absence of consistency and standardization in forensic procedures frequently hinders the effectiveness and dependability of investigations. Implementing standardized protocols can enhance the uniformity and acceptability of digital evidence in legal proceedings, thereby bolstering the credibility of cyber forensics. The discipline can gain more acknowledgment and dependability in the legal and cybersecurity communities by ensuring forensic methods’ strength and widespread acceptance.

2. Related Works

Cyber forensics is a constantly evolving discipline that tackles the difficulties presented by cybercrime. In his paper about cyber forensics from 2020, Yadav [1] comprehensively examines cyber forensics, highlighting its significance and the diverse methodologies employed in cybercrime investigations. The author emphasizes the considerable obstacles, such as the pressing necessity for ongoing enhancements to forensic tools and methods to stay abreast of the ever-changing cyber threats. He proposes that future research should prioritize the integration of artificial intelligence to enhance forensic capabilities and streamline the investigative process, ultimately leading to improved accuracy and efficiency in cyber forensics.

Rakha [2] explores the scientific techniques used in cyber forensics to discover digital evidence that can be used in legal proceedings. The analysis highlights essential challenges, such as the absence of uniformity and comprehensive structures, that impede the efficiency of forensic investigations. The authors propose that future research should focus on developing standardized protocols to improve the reliability and acceptability of digital evidence in legal proceedings. By ensuring the robustness and universal acceptance of forensic methods, the credibility of cyber forensics will be strengthened.

The effectiveness of cyber investigations heavily relies on the usability of digital forensics tools, as Baafi [3] correctly states. He explores the usability concerns associated with different tools and emphasizes the significance of choosing tools based on user requirements. The article addresses obstacles such as integrating tools and usability across various user backgrounds. The authors suggest that future research should prioritize enhancing user interfaces and optimizing forensic tools to make them more accessible and efficient for users with different levels of expertise. This will ultimately improve the usability and effectiveness of cyber forensics.

A paper by Yerriswamy and Vehumadhava from 2022 emphasizes the crucial significance of cyber forensic tools in examining digital crimes. The authors highlight the importance of enacting statutory laws and implementing security measures to bolster forensic investigations. They identify challenges associated with tool selection based on financial constraints and level of expertise. Further investigation should prioritize the development of economical and adaptable forensic instruments and the establishment of uniform protocols for their application in different investigative situations [4].

Dunsin et al. [5] examine the utilization of machine learning algorithms in cybersecurity and cyber forensics. The authors discuss the difficulties in guaranteeing these algorithms’ inclusiveness, genuineness, and efficacy. The authors propose that future investigations should investigate deep learning and computational intelligence to improve forensic analysis and create more sophisticated, adaptable algorithms that can effectively address the intricacies of cyber threats.

In their paper from 2022, Ekhande et al. [6] focus on evaluating the efficacy of deep learning, specifically convolutional neural networks (CNNs), in digital forensics. The authors emphasize the difficulties in modifying deep learning models for different forensic tasks and propose that future research should focus on improving the precision of these models. Using digital images and sound analysis techniques in forensics can significantly enhance their classification, increasing reliability and efficiency.

In their paper from 2022, Javed et al. [7] thoroughly examine the most advanced tools, techniques, and difficulties in digital forensics. The authors emphasize the need for further investigation into the integration and efficacy of forensic tools, proposing that future research should prioritize the development of standardized and interoperable forensic toolkits. This will enhance the efficiency of addressing emerging cyber threats and optimize the overall effectiveness of forensic investigations.

Digital forensics is a subdivision of forensic science that employs digital data as evidence in investigations and legal proceedings, state Kaur and Kaur [8]. This paper comprehensively examines the processes and models involved in digital forensics and the necessity for standardized procedures. The text emphasizes the difficulties in guaranteeing the admissibility and dependability of evidence. Further research should focus on creating comprehensive models for digital forensic investigations that can effectively tackle emerging digital threats and enhance forensic techniques.

A paper by Perilli et al. [9] discusses using data derived from electronic devices as admissible evidence in legal proceedings, explicitly emphasizing the significance of metadata within digital photographs. The article discusses the difficulties in extracting data and protecting it from cyber-attacks. The authors propose that future research should enhance forensic techniques for metadata analysis and establish resilient cybersecurity measures to safeguard against diverse forms of cyber-attacks, guaranteeing digital evidence’s authenticity.

A paper by McCluskey et al. [10] discusses the development of computer forensics, focusing on the necessity of a uniform curriculum and professional benchmarks. Additionally, it emphasizes the difficulties in choosing appropriate tools and following forensic procedures. The authors propose that future research establishes universally recognized criteria, enhances forensic education, and guarantees forensic methods’ reliability and widespread acceptance across various jurisdictions.

In a paper from 2021, Malik [11] explores the use of software in cybercrimes and the challenges posed by increasing reliance on internet-enabled devices. The author discusses various cyber-attacks, including phishing, software piracy, and denial of service, and the mechanisms these attacks exploit. It highlights tools like Kali Linux, Ophcrack, and Safeback, explaining their dual use in ethical (white-hat) and unethical (black-hat) contexts. The paper also emphasizes the role of forensic science in combating cybercrime, detailing tools like EnCase and Safeback, which forensic experts use to retrieve and analyze data from compromised systems. EnCase is noted for its ability to access hard drives, recover data, and provide evidence for legal proceedings, while MD5sum is highlighted for ensuring data integrity and verifying digital evidence. The paper concludes that a lack of public awareness about cybersecurity facilitates cybercrime, stressing the need for education and vigilance to mitigate risks. Challenges include understanding complex software algorithms and the dual-use nature of these tools, which make addressing cybercrime increasingly complex.

Kumar et al. [12] examine cross-site scripting (XSS) attacks and digital forensic models for gathering evidence. The authors discuss the difficulties in detecting and analyzing these attacks and suggest that future research should focus on developing more efficient forensic models and methodologies for collecting evidence. Implementing this measure will improve the capacity to investigate and bring legal action against cybercrimes more efficiently.

A study by Malik [11] comprehensively examines software utilized in cybercrimes and forensic tools employed for investigative purposes. The authors analyze the difficulties associated with comprehending and combating different cybercrime techniques. They propose that future research should prioritize the creation of all-encompassing frameworks for forensic analysis and enhancing software tools to detect cybercrime. These technological advancements will improve the efficiency and efficacy of cyber forensics.

In his 2021 paper, Kolesnyk [13] focuses on the role of digital evidence in preventing IT crimes and provides forensic support in this regard. The authors emphasize the difficulties encountered during the investigation due to cybercrimes’ distinctive characteristics. The authors propose that future studies should enhance forensic methodologies, incorporate cutting-edge technologies, and devise specialized techniques to augment the collection and analysis of evidence in cybercrime investigations.

In their 2020 paper, Qadir and Varol examine how machine learning improves digital forensics by facilitating the examination of large, varied datasets. They also examine how machine learning may forecast and anticipate criminal behavior through the analysis of historical data, hence enhancing efficiency and precision in recognizing possible dangers in digital contexts [14]. Similar conclusions are echoed by Dunsin et al., who evaluated sophisticated AI and ML methodologies in digital forensics, emphasizing applications such as data recovery and cybercrime timeline reconstruction. This study offers a comprehensive evaluation of the advantages and drawbacks of AI in enhancing digital forensics procedures [5].

In their 2023 paper, Kumar and Kumar examine the application of artificial intelligence and machine learning in financial institutions to detect cyber threats. They emphasize how bespoke ML models improve security by forecasting potential threats, illustrating the relevance of AI-driven solutions in delicate digital forensic contexts [15].

A paper from 2021 written by Rajendiran et al. examines several machine learning applications in cyber forensics, particularly in addressing enormous data difficulties. It emphasizes the essential function of machine learning in automating evidence processing and decision-making, therefore assisting investigators in efficiently managing large data volumes [16].

This paper is structured as follows: In the next section, we will describe some basic terms and technologies related to cyber forensics. In the following sections, we will review the scope of the threats, emerging trends, and new techniques in cyber forensics. We will discuss some legal aspects, case studies, and real-world applications. Then, we will discuss potential areas of future research and finish with the paper’s conclusion.

3. Cyber Forensics Fundamentals

Cyber forensics is the scientific field focused on identifying, collecting, preserving, analyzing, and presenting electronic evidence in a manner appropriate for legal proceedings. It entails the utilization of investigative and analytical methodologies to reveal and record digital activities, encompassing data breaches and cybercrimes. The domain connects technology and law enforcement, highlighting the necessity of preserving evidence integrity via standardized protocols and instruments. Its fundamental principles encompass the chain of custody, safeguarding data integrity, and employing rigorous forensic methodologies to achieve precise and dependable conclusions. The first computer crimes occurred in the early 1980s, spawning cyber forensics [17]. To meet the complexity of modern digital surroundings, the area has expanded to include computers, mobile devices, cloud systems, and social media platforms.

Fundamental processes related to cyber forensics are presented in Figure 1:

To provide a better understanding of these processes, they can be defined as follows [18]:

• The Identification Phase of the cyber forensics process entails recognizing and localizing potential sources of digital evidence. Investigators delineate the parameters of the inquiry and identify pertinent data assets, including hard drives, network logs, emails, or other electronic records that may harbor essential information;
• During preservation, efforts concentrate on securing and isolating digital evidence to maintain its integrity and prevent alteration. This phase generally entails generating backups of the original data and utilizing hashing methods to confirm the integrity of the evidence. Utilizing specialized instruments guarantees the preservation of the original data without alteration;
• The analysis phase involves meticulously examining and interpreting the collected evidence. Investigators employ sophisticated forensic instruments to reveal patterns, anomalies, or essential data points. This encompasses recovering deleted files, decrypting encrypted data, and mapping digital activities to reconstruct events;
• Ultimately, the findings are systematically organized and prepared for dissemination during the presentation phase. Investigators produce succinct reports encapsulating the evidence, frequently employing visual aids such as timelines or activity diagrams. These materials are formatted for court proceedings or investigative briefings, ensuring comprehensibility and legal admissibility.

These principles guarantee that the evidence remains unaltered and preserves its integrity throughout the forensic process.

4. Scope of Cyber Threats

The range of cyber threats is vast and perpetually evolving with technological progress. Cyber threats affect organizations and individuals, resulting in considerable financial, reputational, and operational harm. Organizations face ransomware attacks, which can disrupt operations by encrypting essential data, and data breaches that compromise sensitive customer information, resulting in diminished trust and possible legal ramifications. Cyber threats to individuals manifest as phishing attacks, identity theft, and malware infections, compromising personal data and devices, resulting in data loss and privacy infringements. Phishing attacks can facilitate unauthorized access to individual accounts, exposing sensitive information that may be exploited.

The global character of cyber threats is apparent in their extensive influence across various geographical areas. Cyber-attacks’ escalating frequency and complexity underscore the imperative of implementing stringent cybersecurity protocols. Organizations and individuals must stay alert as these threats increase in complexity. Adversaries utilize more sophisticated attack methods that necessitate equally advanced defense mechanisms for effective counteraction. Let us discuss the implications of cyber threats to individuals first.

4.1. Impact on Organizations and Individuals

Organizations can suffer severe consequences because of cyber threats. The consequences encompass financial losses, harm to reputation, disruptions to operations, and legal ramifications. As an illustration, ransomware attacks can suspend business operations by encrypting vital data, resulting in substantial periods of inactivity and financial detriment. Malware infections can potentially jeopardize personal data and devices, resulting in the loss of data and unauthorized surveillance [19]. In addition, data breaches can reveal confidential customer data, leading to a loss of customer confidence and potential legal consequences [20]. Individuals are susceptible to cyber threats, which expose them to the possibility of identity theft, financial harm, and privacy violations. Phishing attacks have the potential to result in unauthorized entry into personal accounts and the exposure of sensitive information.

4.2. Statistical Overview

These statistics provide a clear indication of the widespread occurrence and seriousness of cyber threats on a global scale [21]:

• Cybercrime victim count: The annual count of cybercrime victims is around 556 million, resulting in over 1.5 million victims per day and 18 victims per second;
• Financial impact: The approximate yearly expense of worldwide cybercrime is approximately USD 100 billion;
• Types of cyber-attacks: The prevalent forms of cyber-attacks are malware, accounting for 50% of incidents, followed by criminal insiders at 33%, and theft of data-bearing devices at 28%;
• Geographic impact: Russia and the U.S. play a substantial role in malware attacks, accounting for 39.4% and 19.7% of global malware, respectively;
• Frequency of attacks: The US Navy encounters more than 110,000 cyber-attacks per hour, emphasizing the ongoing danger presented by cyber adversaries.

The statistics highlight the urgent need for robust cybersecurity measures to safeguard organizations and individuals from growing threats [22].

5. Current and Emerging Trends in Cyber Forensics

Cyber forensics is rapidly developing due to the constant progress of technology and the growing complexity of cyber threats. Current developments in cyber forensics involve incorporating artificial intelligence and machine learning to improve investigative abilities, utilizing blockchains to guarantee data integrity, and accepting cloud forensics to manage the increasing prevalence of cloud-based services.

These emerging patterns are transforming the methods used by forensic specialists to collect, examine, and present digital evidence, providing innovative tools and approaches to tackle intricate cybercrimes better. With the increasing prevalence of cyber threats, the importance of cyber forensics in upholding cybersecurity and aiding legal proceedings is growing significantly.

Application of AI

Artificial intelligence and machine learning are revolutionizing forensic capabilities by augmenting the velocity and precision of data analysis. Artificial intelligence, specifically deep learning (DL), employs neural networks to replicate human decision-making processes, allowing forensic investigators to manage large volumes of data effectively. The creation of the Deep Learning Cyber-Forensics (DLCF) framework demonstrates the incorporation of artificial intelligence into cyber forensics. This framework utilizes deep learning algorithms to automate the procedures of evidence acquisition, preservation, analysis, and interpretation. Classification algorithms categorize extensive datasets and identify pertinent evidence, whereas clustering techniques reveal concealed patterns and relationships within the data [18]. Through artificial intelligence, forensic investigations are enhanced, enabling investigators to concentrate on the most crucial elements of the case.

Artificial intelligence and machine learning are crucial in enhancing cyber forensics by facilitating the quick analysis of extensive information and revealing concealed patterns, particularly in cloud environments like Microsoft Azure. AI models, especially deep learning, enhance investigators’ ability to automate anomaly detection, identify intricate cyber risks with increased accuracy, and expedite incident response. Utilizing Azure Machine Learning and Azure Data Explorer, forensic teams may anticipate potential threat vectors and acquire critical insights that enhance and accelerate security responses. Deep learning algorithms, adept at discerning intricate patterns among vast datasets, aid investigators in uncovering previously unnoticed threat flags. AI-driven algorithms in Azure Sentinel enhance forensic operations by aggregating and categorizing various threat types, enabling more effective incident triage. This comprehensive method improves the precision of forensic inquiries while markedly diminishing human mistakes and investigative lags. Consequently, AI and ML technologies augment the reliability and velocity of forensic replies, strengthening cybersecurity measures in cloud-based forensic operations.

The application of artificial intelligence (AI) in forensic investigations is aligned seamlessly with the traditional forensic process outlined in Figure 1, which encompasses the phases of identification, preservation, analysis, and presentation. AI technologies augment each phase, streamline workflows, and enhance the accuracy and efficiency of investigations.

• Identification Phase: AI technologies accelerate the identification of evidence by automating the detection of anomalous behaviors, compromised systems, and potential attack vectors. For instance, anomaly detection algorithms are deployed in Security Information and Event Management (SIEM) systems like Azure Sentinel, allowing for the real-time identification of outliers such as suspicious login attempts or unexpected geographic access patterns. These capabilities directly enhance the speed and precision of the Identification Phase.
• Preservation Phase: In this phase, AI facilitates the automated preservation of digital evidence. Predictive models are leveraged to flag high-risk activities, prompting the immediate snapshotting of volatile data and log capture to ensure forensic soundness. Cloud-native platforms such as Azure Sentinel can be integrated with automated workflows, allowing for the preservation of relevant data streams, thereby mitigating the risk of evidence tampering or loss in dynamic environments.
• The analysis phase is characterized by AI’s transformative role, where processing large datasets with speed and accuracy is rendered invaluable. Machine learning models can analyze user behavior baselines, and deviations indicative of insider threats or advanced persistent threats (APTs) can be detected. For example, rare patterns or low-frequency events that are often critical to forensic investigations can be detected by unsupervised learning models in Azure Machine Learning Studio.
• AI enhances the presentation phase by automating reporting and visualization. AI tools synthesize complex data into concise visualizations, incident timelines, and actionable insights, aiding legal and operational teams. When integrated with Azure ML Studio, tools such as Power BI provide dashboards that clearly present the findings to technical and non-technical stakeholders.

AI-driven technologies bridge identification, preservation, analysis, and presentation, allowing forensic teams to respond to incidents with greater agility, accuracy, and scalability. This alignment ensures that AI is regarded not merely as an auxiliary tool but as a central component in modernizing forensic practices, empowering organizations to meet the demands of increasingly sophisticated and dynamic cyber environments.

6. Methodologies in Azure Forensics

In the evolving landscape of cyber forensics, particularly within cloud infrastructures such as Microsoft Azure, adapting and refining forensic methodologies to address unique challenges is deemed critical. This section is dedicated to the introduction of advanced forensic methodologies implemented in Azure Active Directory (Entra ID), with practical applications and innovations in cloud forensics highlighted.

The collection and analysis of forensic data are conducted with precision and care. The effective aggregation and analysis of logs are fundamentally anchored in forensic data collection in Entra ID. The strategic configuration of log management is involved in capturing relevant data, which is considered crucial for monitoring activities and detecting anomalies. Entra ID provides robust capabilities for log collection, primarily through Azure Monitor and Azure Log Analytics. These tools facilitate collecting, analyzing, and managing vast amounts of logging data for forensic experts.

The data collected typically include user activities, authentication requests, and configuration changes within the Azure environment. Forensic analysts utilize Azure Data Explorer, a highly scalable data analytics service, to ingest, store, and query large datasets from various sources, including the Unified Audit Logs and Entra ID Identity Protection logs. The exploration of intricate patterns and potentially malicious activities is considered vital.

The methodology for utilizing Azure Data Explorer in forensics is presented. The pivotal role of Azure Data Explorer in forensic investigations is highlighted by its capability to analyze large-scale structured and unstructured data. The process is initiated by establishing an Azure Data Explorer cluster and creating a database tailored to forensic needs. Once the database becomes operational, forensic data, including the export of Unified Audit Logs and outputs from Entra ID Identity Protection, are ingested.

Forensic analysts utilize KQL to conduct advanced queries on the data. The queries are designed to uncover hidden patterns and extract actionable insights from the data. For instance, analysts may search for signs of compromised identities or unauthorized access attempts by querying risk detections and Sign-in Logs. This systematic approach ensures a thorough examination of data, which aids in accurately identifying security incidents.

An example of a case study is presented. An example of applying these methodologies is observed during the forensic investigation that follows a real-world breach scenario. Analysts leveraged Azure Data Explorer to sift through gigabytes of log data to identify the origin of the breach. Unusual access patterns were queried from the logs and cross-referenced with known indicators of compromise, allowing for the swift pinpointing of malicious activities and the affected accounts.

Challenges and considerations are presented. Azure provides comprehensive tools for cloud forensics; however, several challenges remain. The dynamic nature of cloud environments is often associated with transient data, which can complicate the preservation of evidence. Furthermore, it is required that both providers and customers understand their roles in maintaining forensic readiness within the shared responsibility model in cloud computing. The integrity of the forensic process in such a setting is ensured through constant updates to forensic methodologies and tools.

Challenges and Solutions in Cloud Forensics

Implementing forensic methodologies in cloud environments, such as Microsoft Azure, is associated with unique challenges. These challenges are attributed to the cloud’s inherent characteristics, including its dynamic nature, multi-tenancy, and data distribution across global infrastructures. This section is intended to outline common real-world challenges encountered during cloud forensics and innovative solutions developed to address these challenges.

Challenge 1: The volatility of data and the nature of temporary resources are acknowledged. In cloud environments, the transient nature of resources, such as virtual machines and containers, can result in volatile data that may be lost upon terminating these resources. This volatility poses significant challenges for forensic data preservation and collection. Forensic teams employ automated tools to trigger snapshots and backups of virtual machines and containers when a potential incident is detected to mitigate the risk of data loss. The capabilities to automate the response to alerts are provided by Azure Automation and Azure Logic Apps, allowing for the capture of data before the scaling down or termination of resources. The automation is designed to ensure that even temporary data are captured and stored for forensic analysis, with adherence to the principles of digital evidence preservation.

Challenge 2: Multi-Tenancy and Data Segregation: The multi-tenant architecture of Azure, although efficient for resource utilization, complicates forensic investigations. The co-location of data from multiple clients may raise concerns regarding data privacy and the potential for accidental exposure during a forensic investigation. To address these concerns, Azure employs strict access controls and encryption to segregate customer data securely. Forensic tools designed for use in Azure, including Azure Security Center, are equipped with features that respect tenant boundaries and ensure that investigations are confined to the data owned by the entity under investigation. Furthermore, Azure’s compliance with international standards such as ISO 27001 [23] ensures adherence to global privacy regulations in forensic activities.

Challenge 3: Management and Integrity of Logs: The critical nature of effective log management for forensic investigations is acknowledged; however, the management and assurance of log integrity within a distributed environment present an inherent challenge. Log data’s completeness, accuracy, and immutability are vital for ensuring forensic validity. It has been observed that Azure offers solutions such as Azure Monitor and Azure Sentinel, which facilitate comprehensive log collection across all Azure services. Additionally, the integrity of these logs is ensured by providing immutable storage options. A wide array of telemetry data are collected by Azure Monitor, which are then processed and analyzed by Azure Sentinel, Azure’s cloud-native SIEM system. These tools support advanced query capabilities and automated response actions, resulting in the enhanced efficacy and efficiency of forensic investigations.

A notable application of these solutions was observed while investigating unauthorized access to an Azure-based application. The forensic team utilized Azure Sentinel to analyze log data and detect abnormal access patterns. The team leveraged Sentinel’s machine learning capabilities to identify and isolate suspicious activities quickly, significantly reducing the impact of the incident.

Anticipation is directed towards future developments. Enhancing forensic readiness is addressed. As cloud environments evolve, the strategies for forensic investigations must also be adapted. Future advancements are expected to focus on strengthening real-time data analysis capabilities and the development of more sophisticated AI-driven tools for anomaly detection. Incorporating forensic readiness into the design of cloud architectures is deemed crucial, with systems being inherently equipped to facilitate forensic investigations.

7. Case Study

Case studies offer essential insights into the practical application of cyber forensics, demonstrating how theoretical concepts are utilized to combat complex cyber threats. By examining incidents, such as ransomware attacks or supply chain breaches, investigators can identify weaknesses in current cybersecurity frameworks and formulate more effective defensive strategies. These real-world instances demonstrate the forensic techniques employed and underscore the significance of prompt detection, comprehensive investigation, and post-incident evaluation to avert future breaches. This case study examines the intricacies and obstacles organizations encounter in addressing cyber-attacks and illustrates how utilizing sophisticated forensic tools and methodologies in cloud environments such as Azure can alleviate damage, restore data, and strengthen defenses against emerging threats. This section delves into the practical applications of cyber forensics by analyzing detailed case studies.

7.1. Forensic Investigation of a Ransomware Attack on an Azure-Hosted Service

A medium-sized enterprise encountered a significant disruption due to a ransomware attack that encrypted vital data across its Azure-hosted services. This incident hindered operational capabilities, and severe data leakage risks were also posed. Consequently, a forensic investigation team was engaged to analyze the breach, pinpoint the attack vectors, and devise strategies to prevent future incidents.

Figure 2 comprehensively illustrates the forensic analysis process undertaken in response to a ransomware attack on Azure-hosted services. The systematic approach, from the initial engagement of the forensic team through to the final mitigation and prevention strategies, is outlined in the Figure 2:

In this case study, the forensic team within Azure Sentinel employed a structured, multi-step analysis to investigate a ransomware breach initiated via a phishing email. The investigation was conducted through systematic log consolidation, targeted KQL queries, and behavioral analysis.

7.1.1. Data Collection and Consolidation

The forensic analysis was initiated by identifying key log sources within the Azure environment, which could provide critical insights into the suspected ransomware breach. The primary logs gathered included Azure Activity Logs, Sign-in Logs, and security events. The logs were essential for tracing user and system activities, access attempts, and configuration changes across the environment. Additionally, customized logs were extracted from specific virtual machines linked to the affected systems, with application-level events being captured that could potentially reveal the attacker’s behavior. The data collection process was carried out in phases, beginning with the initial aggregation of log data from each identified source. The native capabilities of Azure Sentinel were configured to automate the aggregation process, thereby ensuring data continuity and real-time updates as new logs were generated. Pre-processing steps were undertaken to maintain consistency across these various log sources. The normalization of timestamp formats, the standardization of field names, and the removal of redundant data entries were involved, which facilitated smoother correlation during analysis. To streamline data ingestion and reduce manual intervention, predefined connectors were set up in Azure Sentinel to continuously collect and update log data from the Azure Activity Logs, security events, and Sign-in Logs. These connectors ensured that up-to-date information was provided by each log source, which allowed for the effective monitoring and analysis of data by the forensic team. A strong foundation for conducting comprehensive forensic investigations with consistent, reliable data was laid by this integration, coupled with automated ingestion.

7.1.2. Specific KQL Queries and Indicators

Suspicious activities linked to the ransomware breach were identified using targeted Kusto Query Language queries within Azure Sentinel. Specific anomalies and indicators of compromise (IoCs) that could signify unauthorized access or malicious activity were detected by crafting these queries.

The primary query focused on unusual login locations to identify logins from regions outside the organization’s expected operational boundaries. The detection of potential account takeovers was facilitated by filtering sign-ins from high-risk geolocations:

The following table represents the output of the query, highlighting users with multiple suspicious sign-in attempts from unexpected regions:

Anomalous login activities were identified by isolating users who accessed the system from unexpected regions outside the operational zones. Multiple logins by [email protected] from “UnknownRegion1” were noted, which could indicate user travel or credential misuse. Additionally, [email protected] logged in five times from “SuspiciousRegionX”, flagged in prior threat intelligence. Furthermore, [email protected] was observed to have eight logins from “HighRiskRegion”, which raises significant concern due to its administrative privileges. Immediate actions were taken, including investigating associated IP addresses for malicious activity, verifying login legitimacy through user travel or VPN logs, and resetting credentials if unauthorized access was confirmed. During the Identification Phase of the forensic workflow, these anomalies were identified, and critical insights were provided for further machine learning-based analysis to mitigate and prevent similar unauthorized access in the future.

The aggregation of login attempts from non-standard locations was conducted, enabling the identification of accounts accessed from unfamiliar or high-risk regions, which may suggest compromised credentials. A further inquiry was directed towards failed login attempts, as it may be recommended that repeated failures indicate brute-force attacks or unauthorized access attempts. Monitoring for a threshold number of failed logins within a specified timeframe allowed for the isolation of accounts experiencing repeated access attempts:

The query was conducted to highlight accounts that exhibited multiple failed login attempts, which may indicate possible brute-force attempts or attempts to access accounts with elevated privileges:

This query analyzes Windows security event logs (Event ID 4625) to detect accounts with an unusually high number of failed login attempts. This pattern often signals brute-force attacks or credential-stuffing attempts where attackers repeatedly try to access an account using guessed or stolen credentials. The query aggregates failed attempts by user account and originating IP address, isolating instances where more than five failures are logged. The results provide critical evidence for identifying unauthorized access attempts, which can help prevent account compromise and inform subsequent forensic analysis.

To detect privilege escalations, the team implemented a query to monitor additions to high-privilege groups, such as administrators or other critical roles. The identification of newly added accounts within privileged groups was facilitated by this query, which serves as a standard indicator of an attacker’s attempt to gain elevated access:

This query was designed to filter for role changes indicating privilege escalation, particularly when new accounts were added to high-level administrative groups:

The output revealed accounts and roles involved in privilege changes, such as [email protected], where two members were added to the highly sensitive Global Admin role, raising concerns about unauthorized escalation. Similarly, [email protected] added a member to the Security Group Admin role, suggesting potential lateral movement, while [email protected] added three members to the Directory Readers role, possibly for reconnaissance. Immediate actions included verifying the legitimacy of added accounts, auditing their activities, and revoking unauthorized access. These findings, aligned with the Identification Phase, provide critical insights for further investigation and correlation with system activity logs to assess potential compromise of sensitive systems.

7.1.3. Steps for Anomaly Detection

The forensic team employed a combination of thresholds, parameters, and behavioral analysis techniques within Azure Sentinel to pinpoint suspicious activities associated with the ransomware attack. Specific detection criteria were set, allowing for systematically isolating abnormal behaviors.

Multiple failed login attempts within a short period may indicate a brute-force attack. The team established a threshold of five failed login attempts within an hour for each account. An alert was triggered if the threshold was exceeded, indicating that the account was potentially compromised. An example of a KQL query is presented below:

Each one-hour time bin counts the number of failed login attempts per account and IP address. Any account that exceeds five failed attempts per hour is flagged for further investigation, which aids in the team’s effective detection of brute-force attempts.

The code for the Anomaly Detection Phase was enhanced by the addition of temporal granularity and contextual parameters, which are intended to improve the precision and relevance of detected anomalies. The bin(TimeGenerated, 1h) function was introduced in the code, allowing for the aggregation of failed login attempts into specific time intervals. This enables the detection of rapid, concentrated bursts of activity that are characteristic of brute-force or credential-stuffing attacks. It is allowed for forensic teams to identify not just overall patterns of suspicious behavior but also time-bound anomalies that may indicate an ongoing attack. Additionally, it has been observed that filtering by specific thresholds (e.g., more than five failed attempts per hour) results in noise reduction, with a focus placed on significant deviations from baseline behavior. This query is made more effective for uncovering sophisticated, time-sensitive threats, such as targeted account compromise attempts, and actionable insights are provided for deeper investigation and automated alerting.

Logins from various geographic locations within a brief time frame are considered a strong indicator of account compromise. This query identifies accounts logging in from regions not commonly associated with the user’s activity. An example of a KQL query is presented below:

Login events were grouped by user and time bin, followed by creating a list of unique login locations for each hour. If a user logged in from multiple locations within a one-hour timeframe, the account was flagged. This method highlights impossible or suspicious travel patterns, revealing the potential for account takeovers.

Unexpected additions to privileged groups, such as “Domain Admins” or “Enterprise Admins”, indicate attempts at privilege escalation. The team created a query to monitor modifications within the group, specifically focusing on tracking new accounts added to these high-privilege groups. An example of a KQL query is presented as follows:

This query filters operations involving adding members to specific high-privilege roles. Each flagged account was scrutinized for signs of privilege escalation or unauthorized access.

The team established baselines for each account’s typical login patterns to capture anomalous behavior. Standard login times, locations, and access frequency for each user were identified by examining logins over two weeks. A review was initiated for deviations from this baseline. An example of a KQL query is presented below:

Each user’s average login time was established over 14 days, and recent logins (within the past day) were compared against this baseline. Logins recorded more than three hours outside the user’s typical range were flagged as anomalies:

The login behaviors were analyzed by comparing recent login times to the average login time of each user over the past 14 days. Logins that occurred significantly outside the user’s typical time frame were identified, utilizing a deviation threshold of three hours. For example, it was noted that [email protected] logged in at 3:15 a.m., which was significantly earlier than the average login time of 10:00 a.m., suggesting that unauthorized access or unusual activity may have occurred. It was observed that [email protected] logged in at 10:30 p.m., which deviated from the average of 3:00 p.m., while [email protected] logged in at 1:10 a.m., which was outside the normal range of activity for this high-value account. Further investigation is warranted for these anomalies to validate the legitimacy of the logins and assess potential threats. Deviations in login patterns were focused on, enhancing anomaly detection by uncovering behavioral outliers that could signify account compromise or insider threats.

The combination of threshold-based queries with behavioral baselines effectively narrowed down potential indicators of compromise by the forensic team. These methods highlighted critical points for investigation and provided a reproducible framework for detecting anomalies tied to account takeover, brute-force attacks, and privilege escalation.

7.1.4. Phishing Email Analysis

The initial point of compromise in the ransomware attack was identified, leading the forensic team to investigate the presence of phishing emails, which are frequently utilized to gain unauthorized access. Several steps were involved in this analysis, beginning with the detection of phishing emails and then the attacker’s use of stolen credentials.

The phishing email was identified through a thorough examination of email logs within Microsoft 365 conducted by the team. Potential phishing messages were narrowed down by filtering for emails that contained known malicious indicators, such as links or attachments that threat intelligence feeds had previously flagged. An example KQL query is provided in Microsoft 365 Defender:

This query was designed to identify emails classified as phishing attempts, with a particular emphasis on those that contained high-risk attachments or malicious URLs. Once identified, emails, particularly those sent to users in sensitive roles, were flagged for further inspection:

Phishing emails flagged with “Phish” in their threat types were identified by the query, with a focus on those containing high-risk attachments such as .zip, .exe, or .docm, or malicious URLs. An email was received by [email protected] from [email protected] containing a .zip file and a suspicious URL, while [email protected] was targeted by [email protected] with an executable attachment (.exe). A phishing email was received by [email protected] from [email protected], which contained a .docm file and a phishing URL. Potential threats were highlighted by these findings, warranting immediate actions such as the blocking of sender domains, the quarantining of emails, and the alerting of recipients to mitigate risks like credential theft or malware infections.

Following the identification of the phishing email, the subsequent step was to trace the utilization of stolen credentials through this initial compromise. Login events associated with the user who received the phishing email were examined, focusing on tracking IP addresses, locations, and times of login attempts. An example of a KQL query that was utilized for tracking login patterns is presented below:

The login attempts associated with the compromised account were isolated and grouped by location and IP address. Following the phishing compromise, anomalous login locations or frequencies revealed unauthorized access patterns.

The team traced lateral movements within the network, with evidence of compromised credentials, through monitoring abnormal access or privilege escalation activities associated with the compromised account. For instance, any unusual additions to privileged groups by this account were identified as indicators of further compromise. An example KQL query for changes in privileged groups by a compromised account is presented below:

This query explicitly tracks privilege changes initiated by the compromised user. The addition of accounts to high-level roles, such as administrative groups, was flagged, as it may indicate attempts by an attacker to escalate privileges.

To strengthen the analysis, the email metadata and headers were examined to verify the origin and authenticity of the phishing email. Analyzing fields such as Received, Return-Path, and Message-ID allowed the confirmation of the email’s external origin and identified any spoofing attempts. The validation of the compromised account access was found to be related to the phishing vector.

7.1.5. Reproducible Timeline or Workflow

The forensic team documented a detailed timeline and workflow outlining the entire process, from data collection to incident confirmation, to ensure the investigation could be replicated and validated. The structured approach facilitated explicit event tracking and allowed other investigators to follow the same methodology in similar breach scenarios.

The investigation was initiated by configuring Azure Sentinel to ingest data from various sources, including Azure Activity Logs, Sign-in Logs, security events, and custom logs from relevant virtual machines. Data ingestion automation was implemented to capture new logs in real time, ensuring that all pertinent activity was documented as the investigation progressed. (It should be confirmed that all data sources were connected and actively feeding into Azure Sentinel before the initiation of any queries. The comprehensive visibility of the environment was ensured from the outset.)

The team executed targeted KQL queries to identify specific indicators of compromise with data in place. Queries focused on failed login attempts, privilege changes, and unusual login locations. The detection of each anomaly was accompanied by timestamping and logging, which formed the initial indicators of potential compromise. Flagged anomalies, including suspicious logins or privilege escalations, should be cross-referenced with known attack patterns or indicators of compromise (IoCs) to ensure alignment with the case-specific indicators identified in the data.)

The origin of unusual login activities was traced back to a phishing email by analyzing Microsoft 365 email logs. The metadata and header information of the phishing email were examined to validate its role in the attack. Upon identification, the team tracked the use of stolen credentials within the compromised account, with monitoring conducted for signs of lateral movement and privilege escalation. (It should be verified that the compromised account demonstrated a precise sequence of events originating from the phishing email. The phishing email was established as the initial breach point, and the sequence of unauthorized access attempts was validated.)

Access to high-value resources was subsequently gained using the compromised credentials. Changes to privileged groups and lateral movements across systems were monitored, allowing for the establishment of how the attacker advanced within the network. (It is to be confirmed that changes in group memberships or resource access were directly correlated with the activities of the compromised account. The link between the initial phishing compromise and subsequent unauthorized access attempts was reinforced.)

The forensic team compiled a timeline to document the attack sequence clearly, which included each major event: initial phishing email compromise, first unauthorized login, privilege escalations, and any lateral movements. The timeline was composed of timestamps, log sources, query outputs, and validation points for each phase. (The timeline will be subjected to a final review, incorporating cross-validated findings from all queries and log sources. It should be ensured that the collected log data or query results directly support each event in the timeline.)

The investigation was organized into a reproducible timeline with defined validation points, ensuring that each phase was thoroughly documented and could be independently verified by the team. This structured workflow facilitated a transparent forensic investigation, allowing for precise evidence tracking from the initial compromise to the full breach confirmation, thereby providing a robust framework for future incident response efforts.

7.1.6. Conclusions Based on Findings

Upon completing the investigation, the forensic team reached conclusions based on the evidence gathered, corroborated findings, and observed indicators of compromise (IoCs). The systematic approach employed throughout the investigation facilitated the connection of each anomaly with the attack timeline, establishing a transparent and verifiable narrative of the incident.

Each identified event—from the initial compromise of the phishing email to privilege escalations and lateral movements—was cross-referenced with relevant log data to ensure consistency across sources. For instance, the phishing email identified in Microsoft 365 was found to be directly linked to subsequent unauthorized login attempts that Azure Sentinel’s Sign-in Logs flagged. A clear cause-and-effect relationship was established by this correlation, with the phishing email being identified as the point of initial compromise.

Compromised credentials obtained through the phishing email were traced to privilege escalations, during which the attacker added accounts to high-privilege groups. Sequential analysis confirmed that the attacker leveraged stolen credentials to gain additional access, resulting in a coherent narrative of the attack progression.

Focusing on specific anomalies could help detect similar phishing-based compromises early. The team’s effective tracing of unauthorized access patterns was facilitated by monitoring email logs for flagged phishing attempts and behavioral analysis of account activity. Other organizations can replicate this approach by implementing similar logging and query strategies, particularly identifying deviations from user baselines and tracking changes to high-privilege accounts.

The team concluded that a proactive approach to anomaly detection is recommended, emphasizing the importance of continuous monitoring and data correlation across Microsoft 365 and Azure Sentinel. Establishing behavioral baselines for high-value accounts and setting alert thresholds for privilege changes were highlighted as critical early breach detection and containment strategies.

The team proposed further refinement of the existing detection rules and thresholds to improve incident response readiness, emphasizing monitoring abnormal login attempts, multi-region access within short intervals, and privilege changes. Furthermore, integrating threat intelligence feeds was advised to identify known phishing domains or IP addresses associated with malicious activities, which could enhance the early detection of phishing attacks.

The team recommended implementing similar workflows and validation steps as standard operating procedures in future incidents, enhancing repeatability and ensuring a structured, comprehensive response.

After this attack, an overview of the incident was initiated, and the disruption caused by the ransomware was highlighted. Key phases were progressed through, including data collection, in-depth Azure tool analysis, and identifying the initial attack vector via phishing. The containment and mitigation efforts are detailed in subsequent sections, including resetting compromised credentials and implementing just-in-time VM access. The outcomes and recommendations are highlighted in the flowchart, which includes enhancements in security training and the integration of advanced threat protection features. Proactive monitoring and regular security assessments are underscored to fortify the organization against future threats. This visual representation effectively encapsulates the entire process, which provides a clear roadmap of the actions taken and the lessons learned from the cybersecurity incident.

The initial attack vector was ascertained, and the subsequent maneuvers executed by the attackers were analyzed. The extent of the impact on the Azure-hosted resources was assessed, and recommendations to address vulnerabilities and enhance the security framework were provided. The goals aimed to provide a comprehensive understanding of the attack’s penetration and progression, evaluate the damage inflicted on the cloud infrastructure, and develop robust measures to bolster the enterprise’s cybersecurity defenses against future threats.

A systematic approach was employed in the forensic investigation, utilizing Azure’s suite of forensic tools and encompassing several vital phases. Initially, Azure Activity Logs were gathered to trace the operations performed by the attackers. Detailed telemetry data indicative of potential malicious activities was captured using Azure Monitor Logs, and snapshots of affected virtual machines were taken to preserve their states during the attack for further offline analysis. Log data were consolidated and analyzed using Azure Sentinel during the analysis phase. The team employed the KQL to identify anomalies, including irregular login attempts and unexpected external connections. A comprehensive timeline of events was developed to delineate the sequence of attacker activities, from the initial breach through network lateral movements to the deployment of ransomware.

The forensic analysis indicated that the initial breach was facilitated by a phishing email that compromised an employee’s credentials, which were subsequently exploited due to inadequate conditional access policies to obtain elevated privileges. Immediate strategies for containment and mitigation were recommended, including resetting compromised credentials, enforcing multi-factor authentication for all users, and updating firewall rules to limit unusual outbound traffic. Furthermore, it was advised that Azure Security Center’s just-in-time VM access feature be implemented to minimize the attack surface by strictly limiting VM access to necessary instances.

The investigation determined that the root cause of the breach was a combination of social engineering through phishing and misconfigured security controls. Due to rapid detection and responsive measures, the impact of the ransomware was confined to a limited portion of the company’s Azure environment. To prevent future incidents, several recommendations were made, including enhanced security training for employees, routine audits of Azure configurations, and the integration of sophisticated threat protection features within Azure. These measures aim to strengthen the organization’s security posture and reduce the likelihood of similar breaches in the future.

The investigation underscored the importance of proactive monitoring and anomaly detection using Azure tools, which are essential for the early identification of potential threats. Additionally, it highlighted the need for regular security posture assessments using the Azure Security Benchmark to ensure compliance with optimal security practices across all Azure resources. These lessons learned emphasize the necessity of maintaining vigilance and adhering to best practices to safeguard against future cyber threats effectively.

7.2. Enhancing Azure Forensics with Artificial Intelligence

Organizations’ migration of critical infrastructure to the cloud has resulted in an exponential increase in the complexity and volume of security data, necessitating a more sophisticated approach to forensic analysis. It has been observed that traditional rule-based security mechanisms frequently encounter challenges in adapting to the dynamic and evolving nature of cyber threats within cloud environments, where subtle behavioral changes and low-frequency anomalies may serve as indicators of sophisticated attacks. The challenges presented are addressed using artificial intelligence and machine learning, which are recognized as essential tools for modern forensic investigations. Security teams facilitate detection and response to incidents in near real-time with increased accuracy.

The cloud-native Security Information and Event Management and Security Orchestration, Automation, and Response platform, Azure Sentinel, is enhanced through AI-driven capabilities, improving the forensic workflow. Integrating data from multiple sources, applying machine learning models for adaptive anomaly detection, and leveraging advanced analytics through Azure Machine Learning (Azure ML) Studio result in a comprehensive and proactive approach to threat detection and incident response by Azure Sentinel.

The application of AI-enhanced forensic capabilities is demonstrated by presenting a case study involving a sophisticated security incident within a financial organization. In this scenario, an attacker gained unauthorized access to a high-value account ([email protected]) through a phishing email. Subsequently, suspicious activities were observed, including anomalous logins from unusual locations and attempted privilege escalation.

The forensic investigation was conducted across three primary stages, with AI-driven tools in Azure Sentinel being utilized to detect, analyze, and respond to the incident.

• Data Collection and Aggregation: The investigation was initiated through the collection of data from various sources, which included Azure Activity Logs, Azure Monitor, and Microsoft 365 logs. These sources provide detailed records of login attempts, access control changes, and system performance metrics. The centralization of these data in Azure Sentinel resulted in a unified view of the environment for the forensic team, which facilitated the identification of suspicious patterns that may have remained undetected in isolated data silos.
• Machine Learning Models for Anomaly Detection: Data aggregated in Azure Sentinel established behavioral baselines for the compromised account and other critical entities by applying machine learning models. Sentinel’s built-in anomaly detection models were designed to identify deviations in login times, geographic locations, and access frequency, flagging unusual activities that may indicate potential unauthorized access. In this case, the anomaly detection models quickly highlighted the unusual login behavior and privilege escalation attempts associated with the compromised account.
• Advanced Anomaly Detection with Azure Machine Learning Studio: The forensic team in Azure Machine Learning Studio developed a custom anomaly detection model to enhance detection accuracy further. The model based on Isolation Forests was tailored to detect low-frequency anomalies specific to the organization’s operations. The model was deployed as a real-time scoring endpoint, allowing for the continuous scoring of new login events for potential compromise by Azure Sentinel, thereby enhancing the team’s detection of complex or previously unknown attack patterns.

This case study explores Azure Sentinel’s AI capabilities, highlighting the empowerment of forensic teams to adapt to evolving threats, respond to incidents with agility, and improve the overall security posture of cloud-centric environments. This process establishes the critical role of each component of the AI-enhanced forensic workflow—data collection, anomaly detection, and custom model deployment. Actionable insights and automated responses were provided to forensic investigators, significantly reducing the time required to detect and mitigate security incidents.

As demonstrated in this case study, this structured and adaptive approach enables organizations to leverage Azure Sentinel and Azure Machine Learning Studio to transform forensic practices from reactive to proactive. A resilient and scalable defense framework was built, which can address sophisticated threats in today’s cyber landscape. Azure Sentinel and Azure Machine Learning Studio are fundamental parts of the Azure AI-enhanced cyber forensic investigation in Azure, as shown in Figure 3:

The process was initiated by collecting data from Azure Activity Logs and Azure Monitor, which record detailed information regarding user and system activities. The data were aggregated and processed through Azure Data Factory, a pre-processing step that prepares the data for further analysis. The data were directed to the ML Learning Studio, subjected to unsupervised and supervised learning techniques. Unsupervised learning algorithms detected patterns and anomalies without prior data labeling. In contrast, supervised learning models classified these anomalies into predefined categories, such as potential security threats. The outcomes of these analyses were subsequently integrated into Azure Security Center and Azure Sentinel. Azure Security Center utilized the information to enhance threat protection and improve security management across Azure services. The refined data were used by Azure Sentinel, a cloud-native SIEM system, to monitor, detect, and respond to threats in real time, thereby ensuring a comprehensive and proactive cybersecurity posture. The detection and response to potential cyber threats are streamlined through this integrated approach, while resource allocation and strategic focus within the firm’s cybersecurity operations are also optimized.

7.2.1. Data Collection Procedures

Practical forensic analysis within cloud environments initiates the systematic collection and aggregation of data from diverse sources. Azure Sentinel centralizes security-related telemetry by aggregating logs from various cloud-native and hybrid resources, including Azure Activity Logs, Azure Monitor, Microsoft 365, and other critical sources. The centralization of data collection is essential for providing a holistic view of user activities, system events, and network interactions, enabling a more complete and accurate forensic investigation.

Records of all changes to Azure resources, including administrative actions, access modifications, and resource creation or deletion events, are provided by Azure Activity Logs. The critical nature of these logs for tracking configuration changes, identifying unauthorized administrative actions, and pinpointing potential attack vectors within the Azure environment is emphasized. For example, the attempt to escalate privileges by modifying role assignments can be traced through the operation records in Azure Activity Logs. An example KQL query is provided for the identification of suspicious role assignment changes:

This query isolates successful role assignment changes, filtering out authorized users. It is beneficial for detecting unauthorized role modifications that may indicate privilege escalation attempts:

Unauthorized role assignment changes in Azure resources were detected by filtering for successful “MICROSOFT.AUTHORIZATION/ROLEASSIGNMENTS/WRITE” operations that were not performed by known authorized users. Potential privilege escalation activities were highlighted by the results, including modifications of roles in resourceGroup1 by [email protected], changes made in resourceGroup2 by [email protected], and updates to roles in resourceGroup3 by [email protected]. It may be indicated by these unauthorized actions that accounts have been compromised or malicious activity has occurred, necessitating immediate investigation to validate the legitimacy of the role assignments and revoke unauthorized changes. Critical security risks are pinpointed by this query, with support provided for the forensic workflow during the Identification Phase through the flagging of anomalous privilege changes for further investigation and mitigation.

Azure Monitor provides a comprehensive suite of logs capturing resource performance metrics, diagnostic data, and health status across Azure resources. Forensic purposes can be served by configuring Azure Monitor to capture specific events, including high CPU usage, network traffic spikes, or unauthorized access attempts on virtual machines (VMs), which may indicate suspicious activity. For instance, if an attacker attempts to exfiltrate data, identifying abnormal network traffic may be facilitated by analyzing the Network Security Group (NSG) flow logs that Azure Monitor provides. An example KQL query for the detection of abnormal network traffic through Azure Monitor is provided below:

This query indicates the detection of inbound denied traffic from external IP addresses, which may suggest attempted reconnaissance or access from suspicious sources. Unusual traffic patterns were identified, allowing the forensic team to focus on high-risk IPs and potentially compromised resources:

High volumes of denied inbound traffic targeting Azure resources were identified by this query, with a focus on remote IPs that generated over 100 denied requests within one-hour intervals. The potential reconnaissance or attack attempts are highlighted by the results, with 150 denied requests generated by 203.0.113.42 at 10:00 a.m., 200 by 198.51.100.77 at 11:00 a.m., and 120 by 192.0.2.24 at 12:00 p.m. It is suggested that scanning activities or unauthorized access attempts were blocked by network security rules. Immediate actions were taken, including the investigation of flagged IPs for malicious activity, the enhancement of network security rules, and the monitoring of any associated suspicious behavior. The Identification Phase is supported by this analysis, which allows for the detection of potential external threats and the mitigation of their impact before successful intrusions can escalate.

User activities within the Microsoft 365 suite, including email access, file downloads, and login attempts, are captured by Microsoft 365 logs. These logs are considered invaluable in forensic investigations, particularly in cases that involve phishing or unauthorized file access. For instance, it can be revealed through Microsoft 365 logs whether a compromised account was utilized to send phishing emails or access sensitive files, thereby assisting in tracing the attacker’s movements and potential data exposure points. An example KQL query for phishing detection in Microsoft 365 logs is provided below:

This query filters emails flagged as phishing attempts. It displays sender and recipient information and associated URLs or attachments. Identifying the initial phishing vector in a compromise and assessing potential exposure is essential.

The integration of on-premises security logs, including Active Directory (AD) logs and endpoint protection data, is supported by Azure Sentinel, thereby facilitating a hybrid view of security events. The value of this capability is particularly noted in environments where Azure Sentinel monitors both cloud-based and on-premises resources. Forensic investigators utilize security events from Active Directory to track user authentication attempts, account lockouts, and changes to group memberships. These logs facilitate the detection of lateral movement or privilege escalation across cloud and on-premises resources. An example KQL query for the detection of suspicious logins in the Active Directory is given below:

This query captures failed remote login attempts, often associated with brute-force attacks or credential-stuffing attempts on accounts accessible from external networks. Such data facilitate the identification of external threats attempting to access internal resources.

7.2.2. Data Transformation and Preparation Using Azure Data Factory

Data must undergo transformation and preparation in AI-driven forensic investigations before effectively utilizing machine learning models. The provision of a powerful, cloud-based ETL (extract, transform, load) service by Azure Data Factory (ADF) is noted, with the automation of data workflows across multiple sources and environments enabled. The orchestration of data movement, transformation, and preparation through ADF allows forensic teams to optimize data for analysis in Azure Machine Learning Studio. This chapter is focused on the support provided by ADF for data readiness in machine learning through transforming, cleansing, and structuring data, which are aimed at enhancing model accuracy and efficiency.

The preparation of data for forensic analysis and machine learning is often characterized by complexity, necessitating a variety of transformations for the standardization and cleansing of data obtained from multiple sources. ADF addresses these challenges through the following capabilities:

• ADF enables the seamless integration of data from cloud-based and on-premises sources. Data extraction from services such as Azure SQL Database, Azure Blob Storage, Microsoft 365 logs, and third-party security solutions is supported. Forensic teams utilize this capability to consolidate data from all relevant sources into a single pipeline for consistent processing.
• A range of data transformation functions, including data cleaning, aggregation, type conversion, and data enrichment, is provided by ADF and is considered essential for forensic analysis. Raw security logs are refined through these transformations, resulting in a reduction in noise and an improvement in the signal-to-noise ratio. Field formats can also be standardized through transformations, ensuring compatibility with Azure ML Studio models.
• Scalability and Scheduling: It has been observed that data workflows within ADF can be scheduled to execute at defined intervals or triggered in real time. The critical nature of this scheduling capability in forensic analysis is highlighted by the necessity for continuous data preparation, which ensures that machine learning models are provided with the latest data. The scalability of ADF enables the processing of large volumes of forensic data without compromising performance.

In the forensic workflow, a typical ADF pipeline is characterized by a series of transformation steps employed to prepare data for utilization in anomaly detection models. An example of a pipeline designed to transform login event data is presented, which includes steps for data cleaning, normalization, feature engineering, and storage in Azure Blob Storage for access by Azure ML Studio. A pipeline that prepares data for a machine learning model to detect abnormal login activities is presented.

The extraction of login data from Azure Activity Logs, Microsoft 365, and external security logs is initiated as the first step. The Copy Data activity of ADF is utilized to move data from these sources into a staging area, typically in Azure Blob Storage. The capture of fields, including UserPrincipalName, Timestamp, Location, and EventType, characterizes the initial extraction. In the Data Cleaning step, invalid or irrelevant records, such as empty fields or failed logs unrelated to security, are removed. Filters can be applied to ADF’s data flows to exclude specific EventType values, ensuring that only relevant login events are processed. Data normalization involves the standardization of time zones to UTC, while the validation of IP addresses is conducted to ensure conformity to correct formats. The normalization of these fields facilitates the alignment of data from different sources. New features were developed to enable anomaly detection within Azure ML Studio. For example, LoginHour is calculated from Timestamp, LoginFrequency is determined by counting occurrences per user, and LocationCategory is derived based on the location field (e.g., trusted or untrusted regions). The transformed data are subsequently loaded into an Azure Blob Storage container. This structured data repository provides the input for training and testing machine learning models in Azure ML Studio.

A transformation example is presented, wherein ADF’s data flow is utilized to standardize timestamps, generate login frequency features, and categorize login locations:

During the data transformation process, timestamps are normalized to a standardized UTC format, which ensures consistency across events from various sources and enables accurate chronological analysis. The specific hour of each login is extracted to facilitate the detection of unusual login times, as certain anomalies may be more detectable based on time-of-day patterns. Additionally, the daily frequency of logins per user is calculated, which serves as a feature for identifying abnormally high login attempts that may indicate potential compromise. Logins are categorized as “Trusted” or “Untrusted” based on predefined trusted regions. This categorization allows for flagging logins from suspicious or unexpected locations, which may necessitate further investigation.

Upon data transformation completion, the pipeline’s final step is loading the prepared data into a storage location accessible by Azure ML Studio, such as Azure Blob Storage or Azure Data Lake. The loading phase renders the transformed data readily accessible for training and inference within machine learning workflows. The automation of this ETL process by ADF results in Azure ML models consistently having up-to-date data, eliminating the need for manual intervention. An example of an automated ADF pipeline configuration for continuous data loading is given below:

This pipeline configuration is designed to facilitate automated, hourly execution of the ETL steps. Raw login data are moved through staging, transformation, and, ultimately, storage for access by ML models.

7.2.3. Utilizing Data for Enhanced Anomaly Detection

Data transformed and standardized by Azure Data Factory enable Azure Machine Learning Studio to be a powerful tool for deploying advanced machine learning models tailored for anomaly detection in forensic analysis. In this scenario, the data prepared by ADF, which included features such as login time, frequency, and geographic categorization, were utilized to train a model that identifies unusual login activities that may signify unauthorized access or potential compromise.

Forensic teams use Azure ML Studio to design, train, and deploy machine learning models that detect patterns and outliers in complex, multi-source datasets. Anomalies that do not fit the established baseline of user behavior can be identified using algorithms such as Isolation Forests or Autoencoders within Azure ML Studio. Login attempts that occur at unusual hours originate from untrusted regions, or exhibit an unusually high frequency may be classified as anomalies, which warrant further investigation.

Transformed data are leveraged by the model to identify deviations in login behavior based on historical patterns, thus enabling a sophisticated layer of security monitoring that surpasses conventional threshold-based alerts. The integration of ADF and Azure ML Studio was observed to enhance the forensic capabilities of Azure Sentinel through the automation of anomaly detection, the reduction in false positives, and the enablement of real-time analysis of potential threats.

Data that have been transformed are stored in Azure Blob Storage, which Azure ML Studio can access them to train the machine learning model. In this case, an Isolation Forest model, well suited for identifying outliers, was employed to detect abnormal login activities. Anomalies were isolated by this model through the random partitioning of data, enabling differentiation between normal and suspicious behaviors without the reliance on labeled training data.

Example Code for Accessing Transformed Data and Training an Isolation Forest Model in Azure ML Studio:

A connection to Azure Blob Storage was established to access the transformed login events data stored in login_events.csv. The cleaned and engineered features created by Azure Data Factory were contained within this CSV file to facilitate preparation for machine learning analysis. Subsequently, relevant features—LoginHour, LoginFrequency, and LocationCategory—were selected as inputs for the model. These features were selected due to their ability to capture essential behavioral attributes of user login activity, enabling the model to establish standard behavior patterns and detect deviations.

An Isolation Forest model was initialized with a contamination level of 0.01, indicating that approximately 1% of the data are expected to be anomalous. The selected features were utilized for training this model, facilitating the identification of outliers by isolating observations that deviate from the baseline of regular activity. Following the training process, the model scores each data point, with a score approaching −1 indicative of a greater likelihood of abnormal behavior. Anomalies identified through observations were isolated in a separate dataset for further analysis, thereby providing forensic investigators with targeted insights into potentially suspicious login activities:

This Python code demonstrates how to use an Isolation Forest machine learning model to detect anomalies in login behavior. The transformed data, stored in Azure Blob Storage, were accessed and loaded into a Pandas DataFrame. Key features, including LoginHour, LoginFrequency, and LocationCategory, were used to train the model. The Isolation Forest algorithm assigned anomaly scores to each record, with lower scores indicating higher suspicion levels. For example, [email protected] logged in at an unusual hour (3 a.m.) with a high login frequency from an untrusted location, resulting in an anomaly score of −0.75. Similarly, [email protected] exhibited an unusually high login frequency of 50 from an untrusted location, scoring −0.88. These flagged anomalies can be further investigated to validate potential threats, providing actionable insights for the Anomaly Detection Phase of the forensic workflow.

Upon completion of the model training, Azure ML Studio facilitates the deployment of the model as a real-time scoring endpoint. This endpoint can be integrated with Azure Sentinel to monitor login events continuously. New login events from Azure Sentinel are sent to the model for scoring, and alerts for forensic teams are triggered by events identified as abnormal.

Events are continuously scored in real time, facilitating forensic analysts’ quick identification and response to potential compromises, such as unauthorized access attempts. This integration streamlines the forensic workflow, enabling proactive threat detection to be conducted in an automated and scalable manner.

Example Code for Deploying the Model in Azure ML Studio:

The deployment code first establishes a connection to an Azure Machine Learning workspace, which enables access to the machine learning environment necessary for managing and deploying models. The trained Isolation Forest model is registered within the workspace and deployed as a real-time scoring service utilizing an Azure Container Instance (ACI). The deployment comprises a script designated as score.py, within which the inference logic determines the scoring of incoming login events by the model. Upon deployment, a unique scoring URI is assigned to the service. The model is called in real time by Azure Sentinel through this URI, with new login events being sent to the deployed service for immediate scoring and anomaly detection.

7.2.4. Leveraging Machine Learning Models from Azure ML Studio to Enhance Azure Sentinel

Integrating machine learning models from Azure Machine Learning (Azure ML) Studio into Azure Sentinel significantly enhances the capabilities of Azure Sentinel’s forensic and threat detection processes. Azure Sentinel applies advanced, custom-built models trained on historical data to identify anomalies, detect subtle patterns, and respond proactively to emerging threats more accurately than rule-based systems. In this integration, the analytical backbone is provided by Azure ML Studio. At the same time, the operational layer is constituted by Azure Sentinel, which applies the model’s insights to real-time security events.

The native detection capabilities of Azure Sentinel are primarily based on predefined rules and anomaly detection models that emphasize common indicators of compromise. However, custom models developed in Azure ML Studio allow for tailoring detection algorithms to unique environments, resulting in models that exhibit increased sensitivity to specific behaviors or nuanced attack patterns. Low-frequency but high-impact anomalies, such as abnormal login times, atypical access locations, or unusual resource access patterns, are detectable by these models, which often signify advanced persistent threats or insider activity. Through this integration, Azure Sentinel can leverage machine learning to process large volumes of incoming security events, with only the most relevant threats being flagged for immediate investigation.

Upon training and deploying the machine learning model as a real-time scoring endpoint in Azure ML Studio, Azure Sentinel can utilize the model to score new security events continuously. The likelihood of abnormal login attempts can be evaluated through the model’s scoring. Sentinel’s alerting mechanism can prioritize high-risk events based on the model’s scoring, ensuring that security teams focus on incidents with the highest probability of compromise.

Azure Sentinel collects real-time login and activity logs from multiple sources, including Azure AD, Microsoft 365, and on-premises AD logs. The data are routed to the deployed model in Azure ML Studio. The scoring of each event is conducted by the model, utilizing features including login hour, login frequency, and location category. Events are assigned an anomaly score, with higher scores indicative of greater risk. When the anomaly score surpasses a predefined threshold, an alert is raised by Azure Sentinel, which provides security teams with contextual information regarding the flagged event and its corresponding anomaly score.

The following sample code is presented to illustrate the utilization of Azure Logic Apps for calling the deployed model in Azure ML Studio and processing the scoring results in Azure Sentinel:

A login event is retrieved from Azure Sentinel through its REST API at the beginning of the workflow. Essential fields such as UserPrincipalName, LoginHour, LoginFrequency, and LocationCategory are included in this event, serving as inputs for the machine learning model. Upon data extraction, they are transmitted to the deployed model endpoint in Azure ML Studio, where the model computes an AnomalyScore. The likelihood that the login event is suspicious is represented by this score, with higher scores indicating a greater probability of abnormal behavior. When the AnomalyScore exceeds a predefined threshold of 0.7, the workflow triggers a high-risk alert in Azure Sentinel. Contextual information from the model’s output is incorporated into this alert, enabling a focused and informed investigation into the flagged event by the security team:

For instance, a login was performed by [email protected] at 3 a.m. with a high frequency from an untrusted location, which resulted in an anomaly score of 0.85 and triggered a “High-Risk Login Detected” alert. A highly suspicious pattern was exhibited by [email protected], with an anomaly score of 0.92. The automated process is designed to enhance real-time anomaly detection, with the Incident Response Phase being streamlined by providing actionable alerts based on advanced machine learning analysis, enabling security teams to address threats more efficiently.

The integration of real-time scoring from Azure ML Studio into Azure Sentinel is associated with several advantages in the forensic investigation workflow:

• Enhanced detection accuracy is achieved using a custom-trained machine learning model, which allows for identifying nuanced patterns that are challenging to capture using traditional rule-based methods. False positives are reduced, allowing for a focus on events that represent genuine threats by analysts.
• The capability for real-time scoring facilitates the immediate analysis of incoming events, generating alerts for the security team within moments of an abnormal event. The time available for an attacker to exploit compromised accounts or escalate privileges within the system is minimized.
• The manual workload is reduced by implementing automated scoring and alert generation, which streamline the detection process and diminish the necessity for manual analysis of each event. By automating routine evaluations, forensic teams may allocate resources to more complex investigations, improving overall efficiency.

Integrating Azure Machine Learning Studio models with Azure Sentinel enables forensic teams to proactively respond to complex cyber threats. Combining data transformation from Azure Data Factory with custom machine learning models in Azure ML Studio enhances Azure Sentinel’s ability to detect advanced threats, including abnormal login behaviors and atypical access patterns. The integration is associated with improvements in detection accuracy and timeliness, and a scalable, adaptive forensic workflow that can evolve alongside an organization’s security needs is established.

7.2.5. Benefits of Machine Learning and Artificial Intelligence in Azure Security Center

Integrating machine learning and artificial intelligence within Azure Security Center is a powerful enhancement to traditional security approaches. The detection, analysis, and response to threats by Azure Security Center are enhanced by the capabilities of ML and AI, resulting in greater speed, accuracy, and adaptability compared to rule-based systems alone. The transformation of Azure Security Center into a proactive security solution is achieved by applying advanced techniques that enable continuous learning from data, adaptation to evolving threats, and reductions in the burden on human analysts. The primary benefits of leveraging ML and AI in Azure Security Center are outlined below.

The ability of ML and AI to improve the accuracy of threat detection is considered one of the most significant benefits. Traditional security methods, including signature-based detection and static rule sets, often need help to detect new or evolving threats that do not conform to predefined patterns. In contrast, ML models trained on historical data can establish a baseline of normal behavior for users, devices, and applications. These models utilize behavioral baselines to detect subtle deviations indicative of potential security incidents, including abnormal login patterns, unexpected access locations, and unusual data transfers. Anomalies that fall outside typical usage patterns are identified, resulting in a reduction in false positives by ML-driven detection, which aids security teams in focusing on high-priority threats.

The capabilities of AI and ML facilitate the dynamic adaptation of Azure Security Center to new threats through continuous learning. As data are subject to change over time, the periodic retraining of the models within Azure ML Studio on fresh data is facilitated, allowing for the refinement of detection capabilities and the maintenance of effectiveness against emerging threats. For example, it has been observed that as user behaviors shift—whether due to seasonal variations, remote work trends, or organizational changes—baseline updates are made to ML models to accommodate these patterns. The likelihood of false alarms from legitimate changes is reduced through this adaptability while the system maintains vigilance to actual threats.

The responsiveness of Azure Security Center is significantly enhanced by integrating real-time anomaly detection. AI-powered models deployed in Azure ML Studio facilitate the immediate analysis of incoming security events, enabling the detection of and response to suspicious activity by Azure Security Center as it occurs. For instance, when a login event is observed to deviate from an established baseline in terms of time, location, or frequency, it is scored by the model as potentially abnormal. Azure Security Center raises an alert if the anomaly score surpasses a specified threshold, which enables instant notifications to be sent to security teams. This real-time analysis reduces the time for attackers to exploit vulnerabilities, and the response is accelerated, thereby mitigating potential damage.

In Azure Security Center, machine learning models provide a mechanism for automatically prioritizing threats based on risk scores. Anomaly scores are assigned to events, allowing incidents to be ranked based on their likelihood of being genuine security threats. The automated prioritization of alerts is designed to relieve security analysts from the manual review process, thereby enabling a concentration on high-severity threats and complex investigations. A login attempt characterized by a high anomaly score—suggesting a significant likelihood of compromise—would be assigned to a high-priority alert. In contrast, events assessed as lower risk would receive lower priorities. The efficiency and productivity of security teams are improved through reductions in false positives and the automatic ranking of alerts by ML and AI.

As organizations experience growth and their digital environments increase in complexity, it is observed that traditional security methods may encounter challenges in scaling effectively. The scalability of ML- and AI-driven solutions in Azure Security Center enables continuous monitoring across large, distributed, and hybrid environments. Vast volumes of data from multiple sources, including cloud resources, on-premises systems, and third-party integrations, are processed and analyzed by ML models. This capability ensures that all parts of an organization’s infrastructure are monitored uniformly, irrespective of scale. Furthermore, it has been demonstrated that automated ML workflows enable the maintenance of scalability within Azure Security Center while ensuring that the precision and timeliness of threat detection are not compromised.

Advanced persistent threats are frequently characterized by stealth and longevity, which results in challenges for detection using traditional rule-based approaches. ML models within Azure Security Center enhance the ability to identify sophisticated threats by uncovering subtle patterns and correlations that may not be immediately apparent. For example, low-frequency but high-risk activities, such as privilege escalations followed by unauthorized data access, may be involved in an APT. AI-powered models can correlate these events over time, linking seemingly benign activities into a coherent attack chain and alerting security teams before the attacker’s objective is achieved. The dwell time of threats within the environment is minimized through proactive detection, which reduces the potential for data breaches and operational disruption.

Integrating artificial intelligence and machine learning into Azure Sentinel is a transformative approach to forensic analysis in cloud environments, with proactive and adaptive responses to increasingly sophisticated cyber threats being enabled. The centralization of data from diverse sources and the application of machine learning for adaptive anomaly detection result in an enhanced ability to detect subtle and complex attack vectors that are often overlooked by traditional rule-based methods in Azure Sentinel. This case study is exemplified by how AI-driven forensics enables faster and more accurate detection, analysis, and response to incidents, with the platform’s potential to support robust security monitoring being showcased. Through a structured and adaptive forensic workflow, which incorporates advanced anomaly detection models in Azure Machine Learning Studio, the evolution of cybersecurity posture from reactive to proactive is facilitated for organizations. Azure Sentinel is positioned as a critical tool within this AI-enhanced framework for constructing resilient, scalable, and efficient defenses that address the unique challenges presented by today’s cloud-centric cyber landscape.

8. Future Works

Cyber forensics offers a wide range of potential research areas, which are constantly expanding due to the rapid progress of technology and the growing complexity of cyber threats. An important field of study involves incorporating artificial intelligence and machine learning to improve forensic analysis and detect potential threats. Artificial intelligence and machine learning can automate examining vast amounts of data, detect patterns, and forecast potential risks. As a result, they greatly enhance the effectiveness and precision of forensic investigations. AI can be utilized to create advanced tools for identifying and examining malware, phishing attacks, and other cyber threats. These threats are increasingly intricate and challenging to detect using conventional methods [24].

Cloud forensics is an essential field that requires further investigation. As cloud computing becomes more widely used, there is an increasing demand for developing forensic tools and techniques to manage cloud environments efficiently. This encompasses tackling obstacles such as obtaining, safeguarding, and examining data in distributed and virtualized cloud infrastructures. Research in this field could concentrate on creating standardized protocols for cloud forensics and efficient tools for gathering and analyzing evidence from cloud service providers [7].

The field of cyber forensics research holds great promise for the future. Significant focus areas encompass incorporating artificial intelligence and machine learning to improve analysis and identify threats. These research directions will not only tackle existing challenges but also anticipate and prepare for the changing landscape of cyber threats, ensuring that forensic experts are adequately equipped to safeguard digital environments.

9. Conclusions

The rapid advancement of digital technologies and their integration into enterprise environments underscores an urgent need for robust cybersecurity measures. The present paper is enriched by a comprehensive year-long examination of cloud forensics practices within Azure AD, and valuable contributions to the field of cyber forensics are provided. The critical role of structured forensic methodologies and advanced tools in understanding and mitigating cyber threats, particularly in cloud environments, is highlighted.

The application of Azure’s forensic tools in a real-world scenario involving a ransomware attack has been detailed, providing a systematic approach to forensic investigations in cloud infrastructures. The detailed analysis of the attack vectors, the forensic methodologies employed, and the integration of Azure’s sophisticated monitoring tools, such as Azure Log Analytics and Azure Sentinel, are presented as the scientific contributions of this study. These tools have demonstrated essentiality for incident management and developing preventive strategies that enhance organizational resilience against cyber threats.

Furthermore, this paper contributes to the academic discussion on using UALs in cloud environments. It explores the complexities of UALs’ schema and the challenges of analyzing unstructured data. It demonstrates how to effectively parse and query large datasets using Kusto and Azure Data Explorer. This exploration is noted to have significant implications for future forensic practices, as a framework is provided for other organizations to enhance their log analysis capabilities, thereby improving the ability to detect and respond to sophisticated cyber threats.

Furthermore, integrating Microsoft Cloud App Security (MCAS) into the forensic strategy has expanded the understanding of activity logging across cloud services. This adaptation offers a blueprint for other forensic researchers and practitioners to extend monitoring reach across multiple platforms, ensuring comprehensive coverage of all potential threat vectors.

Incorporating artificial intelligence and machine learning into Azure’s forensic capabilities signifies a significant evolution in cyber forensic techniques, especially in cloud settings. AI accelerates incident response and enhances forensic accuracy in identifying complex cyber threats by automating intricate data analytics and improving threat prediction capabilities. This study underscores AI’s capacity to strengthen cyber forensic methodologies, facilitating a more agile and adaptive security framework. These innovations facilitate improved security preparedness and a more robust cloud forensics methodology, establishing new cybersecurity and digital forensics standards.