Agentic AI Security Is Moving From Prompts to Runtime Control

Agentic AI security is no longer only about prompt injection. The risk is now concentrated where an agent can take action: call tools, run code, modify data, trigger payments, or chain work through another agent.

OWASP's Agentic AI work highlights risks such as tool misuse, identity and privilege abuse, insecure inter-agent communication, and cascading failures. NIST's Generative AI Profile for the AI RMF pushes teams to manage risk across the system lifecycle, not just the model layer. CISA and NSA guidance on deploying AI systems securely frames AI as production software that needs secure deployment, monitoring, and operational controls.

CapFence is positioned at the execution boundary. It does not try to make the model perfectly obedient. It decides whether the requested action should be allowed, denied, or routed for approval before the target system is touched.

deny:
  - capability: shell.exec
    command_matches: "rm -rf .*"
  - capability: mcp.filesystem.read
    path_outside: "/workspace"

require_approval:
  - capability: payments.transfer
    amount_gt: 500

allow:
  - capability: docs.search
  - capability: database.read
    environment: staging

The trend is clear: enterprises will need least privilege for agents, replayable evidence, and deterministic authorization around tools. CapFence turns those requirements into local policy and audit primitives.

Further reading:

• OWASP Top 10 for Agentic Applications
• NIST AI Risk Management Framework and Generative AI Profile
• CISA Joint Guidance on Deploying AI Systems Securely