Framework

Operationalizing AI security with the NIST AI RMF

A practical way to connect AI governance principles to policy enforcement and evidence.

Framework8 min readRisk and Compliance, CISO

Governance Needs Telemetry

AI risk frameworks depend on real evidence. Teams need visibility into usage, data flows, runtime events, model behavior, and remediation to show that governance is operating.

Map

Map AI systems, workforce usage, data classes, models, tools, retrieval sources, owners, and business processes. This creates the inventory needed for risk decisions.

Measure and Manage

Use runtime detections, red team findings, policy decisions, and incident trends to measure risk. Manage risk through controls that block, redact, approve, monitor, and remediate.

Sustain the Program

AI governance should be continuous. Update policies as models, applications, regulations, and business adoption change.

More Resources

Continue exploring AI security thinking.

Guide

9 min read

The enterprise guide to AI agent runtime security

A practical security model for agents that retrieve context, reason over data, call tools, and act across business systems.

For CISO, Head of AI, Security Engineering

Executive Brief

8 min read

How CISOs can govern workforce AI without slowing adoption

Policy patterns for shadow AI discovery, prompt DLP, app governance, and employee enablement.

For CISO, CIO, Risk and Compliance

Research Note

10 min read

Red teaming RAG systems for poisoned context and over-disclosure

Testing approaches for retrieval abuse, source trust, sensitive context leakage, and model response drift.

For Security Engineering, AI Product Teams

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See how Kavalan helps security and AI teams govern workforce AI, protect agentic systems, and continuously validate GenAI risk.

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