Active

Periodic Review

The organization shall conduct periodic reviews of AI performance monitoring activities to ensure their effectiveness, efficiency, and alignment with organizational needs. Reviews shall evaluate metrics, drift detection, validation, and optimization against established criteria and best practices, identifying opportunities for performance monitoring improvement.

Requirement ID
AIG-006-R009
Parent Control
Domain Code

Requirement Details & Guidance

AIG-006-R009 · AIG-006
Why This Requirement Exists

This requirement exists to periodically review AI performance monitoring activities, metrics, drift detection, validation, and optimization, ensuring AI systems are governed, secured, assessed, and operated in a controlled and accountable manner.

Implementation Guidance

Establish and document the process to periodically review AI performance monitoring activities, metrics, drift detection, validation, and optimization. Define accountable roles, scope, required inputs and outputs, assessment or approval criteria, decision thresholds, review frequency, monitoring, reporting, exception handling, evidence requirements, and escalation paths. Apply the process across the AI lifecycle and integrate it with information security, privacy, risk, compliance, change management, incident management, and business governance as applicable.

Practical Application & Audit Readiness

AIG-006-R009 · Field Guidance

Business Examples

    Common Audit Findings

      Common Mistakes

        Best Practices & Ownership

        AIG-006 · Recommended Approach

        Recommended Practices

          Typical Owner

          Cross-Mapped Regulatory Frameworks

          Harmonized Control Mappings
          FRAMEWORK CODE FRAMEWORK NAME & DETAILS FAMILY / CATEGORY VERSION ACTION
          SEBI-CSCRF SEBI Cyber Security & Cyber Resilience Framework SEBI’s Cybersecurity and Cyber Resilience Framewor... Clause: 4.1 DE.CM: Security Continuous Monitoring; 1.4 GV.OV: Oversight
          India
          Regulation
          vLatest View →
          ISO27002 ISO/IEC 27002 ISO/IEC 27002 is a guidance standard containing a... Clause: 8.16 – Monitoring activities
          ISO
          Standard
          v2022 View →
          COBIT2019 COBIT COBIT is an enterprise governance and management f... Clause: 4.2 – Align, Plan and Organize (APO) > APO13 – Managed Security > APO13.02 – Define and manage an information security and privacy risk treatment plan.
          COBIT
          Governance Framework
          v2019 View →
          NIST-CSF NIST Cybersecurity Framework (CSF) The NIST Cybersecurity Framework provides high-lev... Clause: GV.OV – Oversight | GV.OV-03 – Organizational cybersecurity risk management performance is evaluated and reviewed for adjustments needed
          NIST
          Framework
          v2 View →
          NIST80053 NIST SP 800-53 NIST SP 800-53 Rev. 5 presents control families, i... Clause: 3.4 – ASSESSMENT, AUTHORIZATION, AND MONITORING; CA-7 – CONTINUOUS MONITORING
          NIST
          Control Catalog
          vRev. 5 View →
          SOC2 SOC 2 Trust Services Criteria SOC 2 is an independent examination and reporting... Clause: CC4.1 – Monitoring of Controls – COSO Principle 16: The entity selects, develops, and performs ongoing and/or separate evaluations to ascertain whether the components of internal control are present and functioning. PI1.1 – Processing Integrity – The entity obtains or generates, uses, and communicates relevant, quality information regarding the objectives related to processing, including definitions of data processed and product and service specifications, to support the use of products and services.
          AICPA
          Audit Framework
          vLatest View →

          Parent Domain Context

          Governance Domain Baseline
          Domain Name:
          AI Governance & Model Security (AIG)
          Purpose:
          To ensure the trustworthy, secure, and ethical development and deployment of AI.
          Scope:
          Enterprise

          Parent Control Context

          Control Baseline
          Control Name:
          AI Performance Monitoring & Continuous Improvement (AIG-006)
          Summary:
          Establishes AI performance monitoring including performance metrics, drift detection, model validation, accuracy monitoring, reliability monitoring, business value assessment, performance reporting, model optimization, review, and continuous improvement.
          Business Description:
          Establish and maintain processes to monitor AI model performance, effectiveness, drift, reliability, business value, operational health, and continuous improvement throughout the AI lifecycle.