Specialist practice ยท AI Security

AI systems introduce attack surfaces that traditional security programs were never designed to cover: prompt injection, tool abuse, data poisoning, and agents acting with more privilege than anyone intended. We assess them the way adversaries approach them.

01

AI Threat Modeling & Security Posture Analysis

A structured review of your AI system โ€” models, data flows, tools, integrations, and trust boundaries โ€” producing a threat model and a prioritized posture report.

๐Ÿ”ต Trust-boundary and data-flow mapping for LLM and agentic applications
๐Ÿ”ต Threat enumeration against the OWASP Top 10 for LLM Applications and MITRE ATLAS
๐Ÿ”ต Control gap analysis and prioritized recommendations
๐Ÿ”ต Alignment to the NIST AI Risk Management Framework and OMB AI guidance

02

Penetration Testing for Agentic AI

Hands-on adversarial testing of AI agents and LLM applications, performed by an OSCP-certified practitioner.

๐Ÿ”ต Direct and indirect prompt injection
๐Ÿ”ต Tool and function-call abuse, privilege escalation, and sandbox escape
๐Ÿ”ต Data exfiltration through retrieval, memory, and output channels
๐Ÿ”ต RAG pipeline attacks: embedding extraction and knowledge-base poisoning
๐Ÿ”ต Findings report with proof-of-concept and remediation guidance

03

MCP & AI Integration Security Review

Model Context Protocol servers and other AI-to-tool integrations are where agents touch your real systems. We review them before they reach production.

๐Ÿ”ต MCP server and client configuration review
๐Ÿ”ต Authentication, authorization, and least privilege for tool access
๐Ÿ”ต Input validation, output handling, and secrets management
๐Ÿ”ต Supply chain review of third-party MCP servers and plugins
๐Ÿ”ต Logging, monitoring, and kill-switch design for agent actions

04

Secure AI Architecture & Governance

Design-stage guidance and governance for organizations adopting AI, so security is built in rather than bolted on.

๐Ÿ”ต Secure reference architecture for LLM and agent deployments
๐Ÿ”ต AI governance policy, acceptable-use standards, and risk acceptance
๐Ÿ”ต Secure DevSecOps for AI/ML workflows and model supply chain
๐Ÿ”ต Executive and board briefings on AI risk

Who this is for

AI startups & product teams

  • Pre-launch assessments, customer-facing security assurance, and investor or enterprise-buyer due diligence.

Regulated enterprises

  • Banks, healthcare, and insurers deploying copilots and agents against sensitive data.

Government & contractors

  • AI risk assessments aligned to the NIST AI RMF and OMB requirements for high-impact AI use cases.

Next step

Request a scoped AI security assessment. We will tell you what we would test, what you will receive, and how long it takes.