Governance Evidence for AI and Agents

Customer security questionnaires, SOC 2 and ISO audits, and new AI regulations all ask SaaS providers to prove how AI features are governed. The evidence is scattered across engineering, security, and product teams, and assembling it for each request slows deals. PointGuard AI centralizes AI inventory, posture findings, and enforcement records and maps them to the frameworks customers and auditors ask about.

AI governance evidence is fragmented, and customers want it quickly.

• Questionnaires now include detailed AI and agent sections.
• Evidence lives in many tools and teams.
• Each framework needs its own mapping of the same controls.
• Answers go stale as AI features change.

AI Governance, AI Security Posture Mgt., and AI Discovery & Inventory centralize evidence and map it to frameworks.

1. Inventory AI features. Maintain a current list of models, agents, and MCP servers, with owners and data access.

2. Collect findings. Combine posture findings, test results, and runtime enforcement into one record per AI system.

3. Map to frameworks. Link controls and evidence to SOC 2, ISO/IEC 42001, NIST AI RMF, and the EU AI Act.

4. Share with confidence. Export current evidence for questionnaires, audits, and trust centers, and track remediation over time.

PointGuard AI supports compliance programs; it does not by itself certify compliance with any framework.

• OWASP Top 10 for LLMs: LLM02 Sensitive Information Disclosure and LLM06 Excessive Agency, supporting oversight of data exposure and excessive authority across AI features.
• OWASP Top 10 for Agentic Applications: ASI03 Identity and Privilege Abuse and ASI10 Rogue Agents, addressing unmanaged identities and agents outside approved policy.
• NIST AI RMF: GOVERN establishes accountability; MAP inventories uses; MEASURE tracks risk; MANAGE documents remediation.