**Introduction**
The rapid integration of AI into core business processes demands a new set of resilience metrics that align with governance and compliance frameworks. In 2025, organizations must translate AI risk into actionable KPIs that satisfy NIST CSF, ISO 27001, and emerging AI‑specific standards.
**Defining AI Resilience Metrics**
* **Model Drift Index** – Quantifies performance loss over time and triggers retraining cycles (NIST, 2023).
* **Adversarial Robustness Score** – Measures model tolerance to malicious inputs, tied to the CIS Control 14.3 framework.
* **Ethical Impact Rating** – Assesses compliance with GDPR Art. 6 and the EU AI Act, ensuring lawful data use.
**Integrating Governance Layers**
Governance committees should embed these metrics in quarterly risk reviews, mapping them to ISO 27001 Annex A controls for technical and organizational measures. For example, the Model Drift Index aligns with A.14.2.6 (Change Management), while the Adversarial Robustness Score feeds into A.18.1.2 (Compliance with legal and regulatory requirements).
**Risk Management in Practice**
Case study: A fintech firm that adopted the Model Drift Index reduced incident response time by 35 % after a regulatory audit (CISecurity.org, 2024). The firm’s governance board linked the metric to board‑level reporting, satisfying CMMC Level 3 audit requirements.
**Conclusion & Call‑to‑Action**
Defining and tracking AI resilience metrics turns abstract governance into measurable compliance. Start by auditing your AI models against the three metrics above, then align them with your chosen framework. Share your progress on LinkedIn or request a tailored audit guide from our cyber resilience team today.
**References**
NIST. (2023). *Cybersecurity Framework*. https://www.nist.gov/cyberframework
CISecurity.org. (2024). *AI Model Auditing Best Practices*. https://www.cisecurity.org/ai-audit
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