A Methodological Framework for AI-Assisted Security Assessments of Active Directory Environments

Abstract

The pervasiveness of complex technological infrastructures and services coupled with the continuously evolving threat landscape poses new sophisticated security risks. These risks are mostly associated with many diverse vulnerabilities related to software or hardware security flaws, misconfigurations and operational weaknesses. In this scenario, a timely assessment and mitigation of the security risks affecting technological environments are of paramount importance. To cope with these compelling issues, we propose an AI-assisted methodological framework aimed at evaluating whether the target environment is vulnerable or safe. The framework is based on the combined application of graph-based and machine learning techniques. More precisely, the components of the target together with their vulnerabilities are represented by graphs whose analysis identifies the attack paths associated with potential security threats. Machine learning techniques classify these paths and provide the security assessment of the target. The experimental evaluation of the proposed framework was performed on 220 artificially generated Active Directory environments, half of which injected with vulnerabilities. The results of the classification process were generally good. For example, the F1-score obtained by the Random Forest classifier for the assessment of vulnerable networks was equal to 0.91. These results suggest that our approach could be applied for automating the security assessment procedures of complex networked environments.

Artificial intelligence for system security assurance: A systematic literature review

“…Graph-based techniques and clustering also play a signi cant role in security assessments. In active directory environments, a methodological framework integrating graph-based and ML techniques effectively assesses security risks by classifying network vulnerabilities based on complex interdependencies and miscon gurations (Article [131]). In the IoT domain, the MPIC model addresses privacy risks by predicting and mitigating the aggregation of PII across IoT devices (Article [80]).…”

“…[85], [150], [102], [126], [158] Vulnerability Classi cation [112], [131], [76], [117] Vulnerability Impact Analysis [199], [66], [99] Vulnerability Analytics [139], [9], [41], [77], [128] Risk Assessment Comprehensive Risk Assessment Framework [23], [48], [95], [175], [5], [22], [75], [62], [179], [122] Quantitative Risk Analysis [176], [65], [192], [7], [196], [195], [89], [189] Risk Classi cation and Prioritization [143], [194], [8] Automated and Dynamic Risk Analysis…”

“…This method outperforms traditional models in precision and robustness. Similarly, Article[131] presents an AI-assisted framework for active directory environments using graph-…”

The intersection of Artificial Intelligence and cybersecurity: Challenges and opportunities

“…Similarly, in vulnerability assessment and management, AI-driven approaches streamline the process of identifying and prioritizing security vulnerabilities within complex IT environments [20]. By analyzing code repositories, system configurations, and historical attack data, AI can pinpoint exploitable weaknesses precisely, allowing organizations to allocate resources more effectively and proactively mitigate the most critical risks [21].…”