Implementation Consulting
Embedded security consulting during your AI build — secure-by-default architecture, guardrails, access controls, and monitoring before you go to production.
Get in touch about this →Why this matters
Retrofitting security into a deployed LLM system is significantly harder and more expensive than building it in from the start. Architecture decisions made early determine what’s possible later.
Most security consultants review systems after they’re built. I prefer to be involved earlier, when the decisions that determine your security posture are still being made.
How it works
You get a dedicated technical resource embedded in your build process — available for architecture review, design questions, security testing of specific components, and regular sessions as the system evolves.
The format is scoped to what you actually need:
Architecture and Design Review
I review your LLM system design before it’s built — data flows, trust boundaries, access controls, prompt architecture, and tool integrations. You get specific recommendations your engineering team can act on.
Ongoing Build Consulting
I participate in your engineering process over the course of your build — available for questions as they come up, reviewing PRs for security implications, and attending architecture discussions. This works on a retainer basis.
Pre-Launch Security Review
A structured review before you ship — testing the system as built against known attack patterns, verifying controls are working as intended, and validating logging and monitoring coverage.
What I bring to your build
Secure Prompt Architecture
How to structure your system prompt, few-shot examples, and context management so the prompt is as robust as possible against injection and manipulation.
Access Control Design
How user identity, permissions, and data isolation should be enforced across your LLM system — including multi-tenant systems, role-based access, and data segregation.
Guardrail Implementation
Evaluating and implementing input/output validation, content filters, and semantic guardrails — and testing them against adversarial inputs.
Monitoring and Incident Response
Logging that gives you visibility into anomalous usage patterns, and response playbooks for when things go wrong.
Agentic System Security
If you’re building agents with tool access, I help design the tool permission model, input validation, output verification, and boundary enforcement that prevents privilege escalation.
Who this is for
- Engineering teams building LLM-powered products who want security expertise embedded in the process, not called in after something breaks
- Organizations deploying agentic systems where the consequence of a security failure is high — customer data, automated actions, financial operations
- Teams without in-house AI security expertise who want a technical partner they can ask questions of throughout the build
Engagement structures
- Architecture review — one-time, a few days of focused work
- Build retainer — ongoing access over the course of your build, typically 2–6 months
- Pre-launch review — structured assessment before you ship
Sounds like a fit?
A short call is usually enough to figure out whether this is what you need and what it would look like.