AI Security Metrics & KPIs: Metrics, KPIs, and Risk Indicators for Effective AI Governance and Board Reporting
Language: English
Published by Independently published, 2026
- Softcover
- New
Seller: California Books, Miami, FL, U.S.A.California Books
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- Title
- AI Security Metrics & KPIs: Metrics, KPIs, and Risk Indicators for Effective AI Governance and Board Reporting
- Author
- Press, AI Security
- Publisher
- Independently published
- Publication year
- 2026
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 13
- 9798181972223
Enterprise AI is scaling faster than most security programs can prove they are working. Boards, regulators, and auditors increasingly ask the same question: How do you know your AI is secure and how do you demonstrate it? Most AI security guidance stops at controls, threat models, or governance frameworks. AI Security Metrics & KPIs fills the gap with a practical measurement playbook: what to measure, how to define KPIs and key risk indicators (KRIs), and how to report results to executives, risk committees, and the board.
Written for CISOs, AI governance leaders, risk managers, security architects, internal auditors, compliance teams, and executive leadership, this book translates AI security into metrics that drive decisions not vanity dashboards. You will learn how to distinguish security metrics from governance metrics, why traditional security KPIs fall short for AI, and how to build a measurement strategy with clear objectives, scope, and accountability.
Inside you will learn how to:
- Design governance metrics for program oversight, policy compliance, AI asset inventory, third-party AI risk, and executive dashboards
- Quantify AI risk with scoring models, KRIs for high-risk models and critical use cases, residual risk measurement, and enterprise risk reporting
- Measure secure AI development: SDLC adoption, testing coverage, red-team outcomes, model assessments, and DevSecOps pipeline controls
- Track model security: exposure, prompt injection, abuse, adversarial attacks, integrity and drift, and model resilience scorecards
- Protect data and privacy for AI: classification, leakage, training-data provenance, RAG security, and regulatory compliance indicators
- Govern agentic AI: agent inventory, activity and tool usage, identity and access, workflow approvals, and multi-agent trust monitoring
- Benchmark maturity across governance, technical security, and operations and improve continuously
- Deliver board-ready reporting: executive dashboards, top KPIs, enterprise scorecards, and evidence for regulators and auditors
- Stand up an AI security measurement program: success criteria, metric selection, automation via SIEM and governance platforms, and ongoing optimisation
The book aligns with real-world programs built on NIST AI RMF, ISO 42001, the EU AI Act, MITRE ATLAS, and enterprise AI governance without replacing those frameworks. It shows you how to operationalise them with indicators leadership can trust.
If your AI investments are growing but your assurance story is not, this is the reference to move from we have controls to we can measure, manage, and report AI security effectively.
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