The Noir OpenAI Guardrails Probe Terminal functions as a continuous AI governance and verification system designed for modern production environments.
Rather than operating as a traditional vulnerability scanner, the platform evaluates how AI systems behave under adversarial, operational, and policy-constrained conditions. It captures runtime telemetry, semantic attack behavior, policy enforcement results, and safety control failures, then transforms that data into Automated Governance Artifacts suitable for engineering, compliance, legal, and security workflows.
The resulting Safety Certificate acts as a tamper-evident operational record of AI system integrity, risk posture, and policy compliance.
This transforms AI safety from a subjective review process into a verifiable infrastructure standard.
API & SDK surface
Control Plane Backend
The Noir Control Plane provides the runtime infrastructure for policy state, OPA simulations, and evidence persistence. Technical specifications for these endpoints are available in the Runtime Control Plane Reference.
/api/entries— registry data proxy./api/discussions— GitHub Discussions proxy when configured./api/policiesand/v1/policy/:policyId— Noir PDP / Bifrost policy distribution./api/policies/:policyId/toggles,/actions, and/audit-export— Policy Manager mutation and evidence export./api/opa/imports,/simulate,/export/:target, and/publish— OPA control-plane workflow.
Client Capabilities
The Noir interface utilizes client-side cryptographic signatures (SHA-256) and WASM-based runtimes (Pyodide) to ensure all policy simulations and integrity checks are executed locally and securely.
- Local SHA-256 certificate hashing for tamper-evident evidence records.
- In-browser policy simulation for repeatable remediation testing.
- Schema validation feedback before policies move into the Control Plane.
Integration Patterns
While the Bifrost Zero-SDK path is the primary enforcement model, the platform is designed for extensible integration with external SDKs and custom remediation pipelines.
Official SDK Packages
Use the SDKs when you want typed client calls for policy distribution, audit export, and control-plane workflows.
Authenticity, integrity, and assessment scope
The Verification Identity Layer establishes the authenticity, integrity, and scope of the assessment. Every scan produces a cryptographically verifiable identity record tied directly to the evaluated system, runtime environment, and enforcement configuration.
Verification ID
Each assessment receives a unique non-sequential verification identifier used for compliance tracking, audit reference, historical comparison, and governance workflows.
NOIR-AUDIT-LH9A-XThe identifier functions as a permanent reference to the exact operational assessment executed by the platform.
Target & Assessment Timestamp
The report records the evaluated endpoint, deployment environment, model provider, policy version, and execution timestamp. This prevents stale or outdated reports from being reused after infrastructure, prompts, models, or policies change.
Signed Integrity Hash
Assessment results are sealed using SHA-256 cryptographic hashing. The generated signature validates findings, evidence, risk classifications, policy outcomes, and remediation status. If any portion of the report is altered, the verification signature immediately becomes invalid.
Governance-readable evidence frameworks
The Compliance Layer translates technical AI security findings into governance-readable evidence frameworks so engineering, security, legal, procurement, and compliance teams can operate from a unified assessment model.
ISO/IEC 42001 Alignment
Maps findings to AI risk monitoring, operational oversight, runtime governance, and documented mitigation evidence.
EU AI Act Readiness
Generates traceable evidence supporting transparency, monitoring, risk documentation, and safety validation history.
OWASP Top 10 for LLM Applications
Maps prompt injection, sensitive disclosure, excessive agency, insecure output handling, model misuse, and tool exploitation into security language.
Scope Note: Noir provides technical evidence attribution to support governance workflows. It serves as a mapping tool for frameworks like ISO 42001 and does not issue legal certifications.
The scanner evaluates semantic attack patterns including indirect injection attempts, roleplay bypass strategies, instruction shadowing, adversarial context poisoning, and system prompt extraction techniques.
Governance Translation Layer
Raw telemetry becomes Automated Governance Artifacts for audit preparation, production sign-off, procurement reviews, vendor validation, security governance, and compliance reporting workflows.
Operational safety posture
The Risk Assessment Summary provides a real-time operational snapshot of AI system safety posture. Each evaluated control is categorized into standardized enforcement states for rapid engineering and governance triage.
- PASS
- REVIEW
- FAIL
Evidence Narrative
Every finding contains contextual evidence explaining why the assessment occurred, how the vulnerability manifested, and what operational risk was introduced.
Potential exposure of internal contact information under adversarial prompting conditions.
Severity Classification
Findings are normalized into Critical, High, Medium, and Low severity categories so organizations can prioritize remediation according to operational impact and governance exposure.
From vulnerability to engineering task
The Remediation Roadmap converts discovered vulnerabilities into actionable engineering tasks. The platform is designed not only to identify unsafe behavior, but to accelerate remediation and policy hardening.
- Enable PII masking.
- Enforce structured outputs.
- Restrict unsafe tool execution.
- Implement prompt isolation.
- Strengthen schema validation.
- Apply runtime guardrails.
The remediation layer is designed for Jira, GitHub, GitLab, sprint planning, release management, and security review pipelines.
Continuous assurance, not one-time scanning
AI systems evolve through model updates, prompt modifications, integration changes, retrieval drift, and policy regressions. The platform continuously evaluates those changes over time.
Scheduled Assessments
Recurring scans identify safety drift, prompt injection exposure, policy degradation, compliance regression, and unsafe runtime behavior.
Runtime Enforcement Without Performance Penalty
Runtime enforcement through the Bifrost policy layer is designed to operate with effectively zero additional request latency, making governance part of the infrastructure layer rather than an external bottleneck.
Historical Comparison View
Sprint 1 → Grade D
Sprint 3 → Grade C+
Sprint 6 → Grade B+Live operational trust indicator
The Seal of Verification functions as a live operational trust indicator for AI systems. Unlike static compliance badges, the Noir verification seal reflects the real-time safety posture of the evaluated deployment.
Dynamic Verification Badge
Organizations may embed live verification badges into applications, internal dashboards, developer portals, compliance systems, or public trust pages.
Live Policy Synchronization
The verification seal is API-driven and synchronized against current scan status, enforcement policy state, remediation history, and runtime validation results. If assessments expire, policies become invalid, enforcement fails, or risk posture degrades, the seal transitions to Monitoring Required, Remediation Required, or Expired.
Probe → Vault → Forge → Control Plane
The Noir OpenAI Guardrails Probe integrates directly with The Forge and Control Plane to create a closed remediation and validation loop.
- Run The Probe against a target AI endpoint.
- Generate an Evidence Vault Safety Certificate with signed evidence.
- Deploy findings into The Forge remediation bridge.
- Reproduce failures and validate mitigation strategies.
- Publish policy state through the Control Plane or OPA engine.
Secure your runtime
Ready to secure your runtime? Start with The Probe to identify your current risk posture.