Can AI review evidence reliably enough to trust it?
AI can significantly enhance the efficiency of evidence review for frameworks like CMMC and SOC 2 by automating initial data processing and anomaly detection. However, it cannot reliably replace human judgment for final attestation. Human assessors remain crucial for interpreting contextual nuances, assessing control effectiveness, and making risk-based decisions, ensuring the integrity and trustworthiness of compliance outcomes.
AI tools excel at processing large volumes of structured data, such as system logs, configuration files, and vulnerability scan results, identifying patterns, and flagging deviations from established baselines or policy requirements. For CMMC and SOC 2, this capability can streamline the initial evidence collection and pre-screening phases, reducing the manual effort involved in identifying missing documentation or obvious non-conformities. This allows human assessors to focus their expertise on more complex, qualitative aspects of the review, improving overall assessment efficiency.
Despite these efficiencies, AI lacks the capacity for subjective interpretation, critical thinking, and the nuanced understanding of an organisation's operational context required for a comprehensive control assessment. Human assessors are indispensable for evaluating the intent behind policies, the effectiveness of implemented controls in practice, and the overall risk posture. Final attestation for both CMMC and SOC 2 necessitates professional judgment and accountability, which currently reside solely with human practitioners, ensuring that compliance claims are robust and defensible.
Sources
- CMMC Assessment Process (CAP) Guide, Version 2.0, Section 3.1 (Assessor Responsibilities)
- AICPA Trust Services Criteria for Security, Availability, Processing Integrity, Confidentiality, and Privacy (TSP section 100, AT-C section 205)