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AI Governance & Red-Teaming

Safe, compliant, and trustworthy AI — validated before and after it reaches production.

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How we validate AI

Every output, checked.

AI output runs a gauntlet of governance gates — most pass, risky ones get flagged. Tap a gate.

AI OUTPUTflaggedEvaluateBias & FairnessSafetyPrivacyAudit & Approve
Evaluate — Structured output-quality evaluation across representative, real-world scenarios.
Our own product
CodeMerlin.ai

We don't just advise on AI governance — we ship it. CodeMerlin.ai is our AI-native engineering governance platform, putting these practices to work every day.

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Overview

AI governance & red-teaming.

We treat AI governance as a first-class engineering discipline, not a checkbox — adversarial red-teaming for prompt injection, jailbreaks, and data exfiltration, paired with structured bias and output-quality evaluation across representative scenarios.

Governance frameworks map directly onto your existing compliance posture — including our ISO/IEC 27001-aligned information security management system — so AI governance reinforces, rather than duplicates, your security program.

What's included

Adversarial red-teaming

Systematic testing for prompt injection, jailbreaking, and data leakage.

Bias & output-quality evaluation

Structured scoring across representative, real-world scenarios.

Guardrails & policy

Output controls and policy enforcement outside the model, deterministic and auditable.

Audit & compliance mapping

Logging and evidence mapped to ISO/IEC 27001 and your existing controls.

What we do

Governance as an engineering discipline.

Validation before launch and continuous assurance after — not a one-time sign-off.

01

Adversarial red-teaming

Probing for prompt injection, jailbreaks, and data exfiltration before attackers do.

02

Bias & fairness evaluation

Measuring and reducing biased or unfair behavior across groups.

03

Output-quality evals

Structured, repeatable scoring of accuracy and helpfulness.

04

Guardrails & policy enforcement

Deterministic controls outside the model that enforce your rules.

05

Audit logging & traceability

Every prompt, output, and decision recorded for review.

06

Compliance mapping (ISO 27001)

Aligning AI governance with your existing security and compliance posture.

Trust, made measurable

Why governance is worth it.

Governance turns 'we hope it's safe' into evidence you can show users and auditors.

Catch failures first

Find unsafe or low-quality behavior before your users — or regulators — do.

Defend the OWASP LLM Top 10

Coverage against the known, exploited classes of LLM attacks.

Reduce regulatory risk

Documented evaluation and controls that stand up to scrutiny.

Reinforce existing security

Built on your ISO/IEC 27001 foundation, not a parallel program.

Evidence for auditors

Logs, model cards, and reports that prove due diligence.

Confidence to ship

Teams move faster when they can trust — and demonstrate — that AI is safe.

Governance capabilities

The depth behind trustworthy AI.

Security, evaluation, and compliance engineering for AI systems.

Red-teaming
Prompt-injection testing
Jailbreak resistance
Bias & fairness
Output evaluation
Guardrails
Policy-as-code
Audit & lineage
PII protection
Model cards
Compliance mapping
Continuous monitoring
Modern governance stack

Tools & standards we work with

Red-team, guardrail, and evaluation tooling mapped to recognized standards.

Red-team
Garak
PyRIT
Custom harnesses
Guardrails
LLM Guard
Prompt Shields
Llama Guard
Evaluation
Ragas
promptfoo
DeepEval
Privacy & Security
Vault
DLP
PII detection
Monitoring
LangSmith
OpenTelemetry
Standards
OWASP LLM Top 10
ISO/IEC 27001
Our approach

How we deliver AI governance

1

Map risks

Profile your AI's threat model, data sensitivity, and compliance needs.

2

Red-team

Adversarially test for injection, jailbreaks, and leakage.

3

Evaluate

Score bias, fairness, and output quality on real scenarios.

4

Add guardrails

Deploy deterministic controls and policy outside the model.

5

Monitor & audit

Log, watch, and re-test continuously as the system evolves.

200+
Projects delivered
50+
Worldwide clients
120+
Skilled experts
2017
Building production AI
FAQ

Common questions

What is AI red-teaming?+
Adversarially attacking your own AI — prompt injection, jailbreaks, data-exfiltration attempts — to find weaknesses before real attackers or users do.
What's the OWASP LLM Top 10?+
A community standard for the most critical LLM risks (prompt injection, data leakage, poisoning, and more). We test and defend against it.
Does this help with compliance?+
Yes. We map governance to your existing posture — including ISO/IEC 27001 — and produce audit-ready evidence, reinforcing rather than duplicating your controls.
Before or after launch?+
Both. We validate before production and monitor continuously after, since models, data, and threats all keep changing.

Make your AI trustworthy.

Consultation is free. Let's pressure-test your AI and prove it's safe to ship.

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