Modern AI products inherit traditional application risk and add new failure modes around model behavior, data handling, tool access, agent autonomy, and user-generated input. Effective AI security work has to account for all of them together.
What this covers
- Reviews of LLM-powered application flows, retrieval patterns, agents, and tool execution paths
- Analysis of prompt injection exposure, insecure output handling, data exfiltration pathways, and privilege boundaries
- Threat modeling for AI features before launch or during redesigns
- Guidance on safe defaults, logging, evaluation, monitoring, and rollout controls
- Practical governance advice aligned to how product and engineering teams make release decisions
How engagements work
AI security engagements typically begin with architecture review and feature walkthroughs. That is followed by trust-boundary analysis, threat modeling, and targeted validation of high-risk areas such as retrieval pipelines, admin features, model tools, and cross-tenant access patterns.
The output is designed to help engineers act. Findings are translated into specific design changes, validation checks, and backlog-ready remediation items rather than abstract concerns.