Service · Guardrails & Evals · Defend

AI Guardrails & Evals Review

Identify guardrail gaps, eval coverage failures, and release-criteria blind spots before they reach production.

A structured review of prompt guardrails, output filters, eval test suites, regression coverage, and release criteria for AI features and agentic workflows. Produces a Guardrails & Evals Review Memo with gap findings, safe and unsafe response classifications, regression test recommendations, and CI-enforceable release criteria.

Offer · AI Guardrails & Evals Review

Timeline

First findings in 5 business days. Review memo in 5–10 business days.

Primary output

Guardrails & Evals Review Memo

Best for

Security, engineering, and product teams shipping AI features with guardrails or evals in place.

Review Guardrails & Evals Scope Agent Hardening

Best for

AI Product Lead, Product Security, Trust and Safety, Engineering Lead

Engagement model

assessment

Duration

2-5 weeks

Deliverables

5 deliverables

What it covers

Guardrail architecture, safety policy, refusal, fallback, and monitoring review

Eval suite, abuse case, failure mode, and regression coverage review

Prompt/control regression testing and release quality gate recommendations

Engineering-ready remediation plan for guardrails, evals, and release criteria

Use when

Guardrails exist but coverage gaps and failure modes are unclear.
The eval suite doesn't catch unsafe outputs or policy bypasses consistently.
The team needs CI-enforceable release criteria before the next AI feature ships.

Supporting products

SecEng Code Scanner

Code-derived validation cases and release-gate candidates.

SecEng Runtime Proxy

Capture and replay behavior for regression analysis.

SecEng RAG Test Harness

Retrieval and authorization eval coverage.

SecEng Program Blueprint Kit

Release-gate and evidence requirements.

Related research

AI Product Security in the Age of Mythos

Product-security framing for guardrails and evals.

AI Security Engineering Handbook

Control and release-gate reference material.

Related people

David Wolf

Builds operating models, controls, detection, and evidence layers for enterprise AI adoption.

Alex Eisen

Finds real AI attack paths through applied vulnerability research, adversarial testing, and incident-informed threat analysis.

Alex Karoulias

Engineering student at Athens Technical University, Class of 2027

Tim Kerimbekov

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Start here

Scope this review through discovery, then translate the result into engineering work, buyer-ready evidence, or a follow-on engagement.

Review Guardrails & Evals

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