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AI security

AI Security

Independent security assessment, adversarial testing and governance assurance for AI systems, GenAI applications, LLMs, RAG architectures and agentic AI.

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Security assurance for AI systems, models and AI-enabled products

AI changes the attack surface of an application. Security risks can arise from the model itself, the data and retrieval layer, prompts and system instructions, agent tools, APIs, plugins, identity and access controls, supply-chain dependencies, deployment architecture and the human workflows around the system. SecurityAttest® assesses these layers together rather than treating AI as an isolated model-security problem.

Typical objective: understand how an AI system can be manipulated, how sensitive data could be exposed, how excessive autonomy could be abused, and whether governance and technical controls are proportionate to the real business risk.

What we assess

AI architecture & threat modelModels, data flows, RAG components, vector stores, external tools, APIs, trust boundaries and privileged actions.
LLM & GenAI security testingPrompt injection, jailbreaks, sensitive-information disclosure, insecure output handling, excessive agency, model and tool abuse.
Data & model supply chainTraining and inference data exposure, third-party models, packages, model artifacts, poisoning risks and dependency provenance.
Agentic AI controlsPermission boundaries, tool invocation, action approval, memory, autonomous workflows, identity propagation and safe failure modes.
AI governance & complianceControl design and evidence aligned to ISO/IEC 42001, ISO/IEC 23894, NIST AI RMF and applicable EU AI Act obligations.
Secure AI lifecycleSecurity requirements, testing gates, deployment controls, monitoring, incident response and change management across the AI lifecycle.

Assessment outputs

Engagements can combine governance review and hands-on adversarial testing. Findings are risk-ranked and linked to concrete remediation actions. Deliverables typically include an executive summary, technical findings with reproducible evidence, an AI threat model, control-gap analysis, prioritized remediation plan and, where agreed, a retest of corrected issues.

Useful for

  • Generative AI applications and copilots.
  • RAG systems using internal or customer data.
  • AI agents that can call tools, APIs or business systems.
  • AI-enabled SaaS products and embedded AI features.
  • Organizations preparing an AI governance or ISO/IEC 42001 program.
  • High-impact AI deployments that need independent security evidence before production use.

Need defensible security evidence?

Share the target system, technology or framework and the decision you need to support. We will define a focused assessment with clear outputs.

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