openclaw-guardrails

Security guardrails for AI agents are enforced through real-time interception of dangerous tool calls and automated remediation. This approach prevents prompt injections and data leaks by automatically reverting unsafe changes with timestamped backups.

SBOM risk scoring evaluates third-party dependencies by linking vulnerability data to a software bill of materials inventory. It monitors extensions across the ecosystem to provide zero-trust auditing and ensure continuous compliance.

Self-healing remediation automatically reverts unsafe configuration changes by enforcing policy boundaries. It creates timestamped backups during rollback to maintain full forensic traceability and ensure system stability.

Yes, the guardrails system enforces MLPS 2.0 compliance and zero-trust auditing through a suite of scripts and a guardrails.yaml policy file. It schedules daily parallel immune scans to continuously monitor boundaries.

Yes, you can use pyyaml to configure custom security policies by defining rules within the guardrails.yaml policy file. This allows flexible enforcement of safe boundaries tailored to your specific deployment requirements.

The best way to automate daily security scans is to schedule the daily immune scan to run automatically. This performs parallel scans across the ecosystem to intercept threats, score SBOM risks, and maintain compliance.