AI Security & Governance
Secure your LLMs, ML models, and AI-powered products end to end — from adversarial testing through governance, compliance, and continuous risk monitoring.
Security built for the AI stack
As organisations embed LLMs, copilots, and machine learning models into core products, the attack surface has shifted from traditional infrastructure to prompts, training data, model weights, and third-party AI supply chains. Our AI Security practice addresses this new layer directly.
We combine offensive testing (adversarial and red-team assessment of your AI systems) with defensive engineering (secure architecture, guardrails, monitoring) and governance advisory (policy, risk registers, regulatory alignment) into a single, coordinated service.
Engagements are tailored to how you use AI — whether you’re deploying third-party LLM APIs, fine-tuning open models, building RAG pipelines, or operating in-house ML systems — and cover both pre-deployment assurance and ongoing operational security.
Coverage across the AI lifecycle
LLM & AI Penetration Testing
- Prompt injection & jailbreak testing
- Indirect prompt injection via RAG / tools
- Model extraction & inversion attacks
- Adversarial input & evasion testing
- Training data leakage assessment
- Guardrail & content-filter bypass testing
AI Architecture & Guardrail Review
- RAG pipeline & vector store security review
- Agent & tool-calling permission boundaries
- API key, secrets & access control hardening
- Output validation & sandboxing design
- Rate limiting & abuse prevention controls
Data & Model Security
- Training & fine-tuning data poisoning assessment
- Model weight & artifact storage security
- PII / sensitive data handling in prompts & logs
- Model versioning & integrity verification
AI Supply Chain Risk
- Third-party LLM & API provider risk assessment
- Open-source model & dependency vetting
- Vendor due diligence for embedded AI features
- License & data-residency review
AI Governance & Compliance
- AI risk register & impact assessments (AIIA)
- Policy development for responsible AI use
- ISO / IEC 42001 (AI Management System) readiness
- EU AI Act & NIST AI RMF gap assessments
- Model card & documentation standards
- AI incident response planning
- Employee AI-usage policy & awareness training
- Continuous AI risk monitoring dashboards
How we secure your AI
Discovery
Map every model, agent, prompt pipeline, and third-party AI dependency in scope.
Threat Modelling
Identify risks specific to your AI use case using MITRE ATLAS and OWASP LLM Top 10.
Adversarial Testing
Actively attack prompts, models, and guardrails to validate real-world resilience.
Governance Review
Assess policies, documentation, and controls against relevant AI regulations and standards.
Remediation
Deliver prioritised fixes plus a roadmap for ongoing AI risk monitoring.
What you gain from this engagement
Adversary-Grade Testing
We attack your AI systems the way real adversaries do, uncovering prompt and model weaknesses before launch.
Regulatory Readiness
Get ahead of the EU AI Act, ISO 42001, and NIST AI RMF with documented, audit-ready evidence.
Practical Guardrails
Architecture and control recommendations your engineering team can implement directly, not just theory.
Continuous Risk Visibility
Move beyond a point-in-time report with an ongoing view of your AI risk posture as models and usage evolve.
Cross-Functional Reporting
Technical findings for engineering teams alongside governance summaries for legal, risk, and leadership.
Vendor & Supply Chain Assurance
Confidence in the AI tools and models you depend on, with clear visibility into third-party risk.
Ready to secure your AI systems?
Request a scoping call with our AI security team. We’ll define the right assessment for your models, pipelines, and governance needs within 48 hours.
Request a Scoping Call →