What we assess.
- LLM application security
- AI agent security
- Model and data security
- Prompt injection risk assessments
- AI tool and MCP integration security
- AI supply-chain security
- Data leakage prevention
- Adversarial testing
- AI infrastructure security
- Autonomous agent permission and isolation reviews
From findings to improvement.
A clear view of trust boundaries, data exposure, tool access, and failure modes across your AI system.
A scoped, collaborative engagement.
- Understand the systems, assets, and business context.
- Agree on scope, authorization, methods, and confidentiality.
- Conduct the technical assessment and document evidence.
- Prioritize findings and coordinate remediation guidance.
- Agree on follow-up validation and continuing assessment.
As AI capabilities advance, security must account for the entire system: model behavior, untrusted inputs, tool execution, permissions, isolation, data flows, and infrastructure.