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AI Security

Secure the AI you're building and buying.

AI is moving fast into production. Prompt injection, data leakage, model access controls, and agentic risk are real problems — not future ones. Tenodex brings practical cybersecurity experience to the unique challenges of AI-integrated systems.

What we do

Three focused AI security capabilities.

01

AI Security Review

A structured review of how your organisation uses AI — covering integrations, API access, data flows, prompt handling and control gaps.

Typical focus

  • AI integration threat modelling
  • Prompt injection risk assessment
  • Data inputs, outputs and leakage paths
  • Authentication and authorisation for AI endpoints
  • AI supply chain and third-party model risk
  • Audit and logging requirements

Deliverables

  • AI risk register
  • Control gap report
  • Priority remediation actions
  • Architecture review notes
  • Executive summary
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02

AI Red Teaming

Adversarial testing of your AI systems — finding what breaks before attackers do. Covers prompt injection, jailbreaking, data extraction and indirect attack paths.

Typical focus

  • Direct and indirect prompt injection
  • Jailbreaking and instruction override
  • Sensitive data extraction attempts
  • Privilege escalation via AI agents
  • Retrieval-augmented generation (RAG) attacks
  • Model output manipulation

Deliverables

  • Red team findings report
  • Exploitability ratings
  • Evidence of successful attacks
  • Remediation recommendations
  • Retesting guidance
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03

AI Infrastructure Hardening

Security controls for the infrastructure running your AI workloads — APIs, pipelines, model storage, credentials and compute environments.

Typical focus

  • LLM API endpoint security
  • ML pipeline and data ingestion controls
  • Model storage and access management
  • Secrets and credential hygiene for AI services
  • Container and cloud security for AI workloads
  • Logging and monitoring of AI interactions

Deliverables

  • Infrastructure risk assessment
  • Hardening recommendations
  • Control implementation checklist
  • Logging and alerting configuration guide
  • Architecture improvement notes
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Why it matters

AI changes the attack surface. Your security program needs to keep up.

Prompt injection is the new SQLi

Any system that passes untrusted content to an LLM is vulnerable. Organisations deploying AI without prompt injection controls are exposed in ways traditional security tooling won't detect.

AI agents need least-privilege too

Agentic AI systems that can read files, send email, query databases or call APIs need the same access controls as human users — and most don't have them.

Third-party AI is a supply chain risk

Using a cloud LLM, an AI SaaS product, or a fine-tuned model from a vendor introduces trust and data exposure risks that need to be assessed — not assumed.

Who this is for

Organisations that are integrating AI into products or internal tools, deploying LLM-based applications, building agentic workflows, or procuring AI-enabled SaaS. Also relevant for boards and risk teams needing assurance over AI risk exposure.

Our approach

We bring standard cybersecurity rigour — threat modelling, control assessment, adversarial testing — to AI-specific risk. No AI hype. Just practical assessment of what's exposed and what to fix.

Guidance

Free resources on AI security.

Ready to start?

Book a briefing to discuss your AI security posture.

We'll give you a straight read on what's exposed, what matters most, and what a practical engagement would involve.

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