Framework

Mapping OWASP LLM risks to enterprise controls

How security teams can translate LLM risks into runtime, workforce, gateway, and red team controls.

Framework9 min readSecurity Engineering, Risk and Compliance

From Taxonomy to Action

Risk lists are useful, but enterprise teams need controls that operate inside real AI workflows. Map each risk to inspection points, policy decisions, evidence, and remediation owners.

Runtime Controls

Prompt injection, sensitive disclosure, excessive agency, and insecure tool use require runtime decisions. The system must understand context, action severity, and data exposure.

Workforce Controls

Shadow AI, data leakage, and unapproved tools require browser and app governance. Employees need approved paths and feedback when sensitive data is at risk.

Testing Controls

Red teaming validates whether controls work against realistic attacks. Findings should be risk-ranked and retested after remediation.

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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