Most AI consultants have never fought an intrusion. Most security consultants have never shipped an LLM system. I do both - designing, building, and breaking AI so yours is the kind attackers give up on.
Nine flagship engagements covering the full AI lifecycle - from architecture and development to adversarial testing, governance, and AI-augmented cyber defense.
Security-first blueprints for AI platforms: trust boundaries, model isolation, data-flow threat models, identity for agents, and zero-trust patterns designed for LLM and ML workloads.
ExploreCustom LLM applications built secure by default - RAG pipelines over your knowledge base, fine-tuning, model selection, evals, and API integration with guardrails engineered in from day one.
ExploreAdversarial testing of AI systems: prompt injection, jailbreaks, data exfiltration through tool use, training-data poisoning, and model theft - mapped to OWASP LLM Top 10 and MITRE ATLAS.
ExploreBring AI into your defense: LLM-assisted triage and enrichment, detection-as-code copilots, alert summarization, and automated investigation playbooks that multiply your analysts.
ExploreShip GenAI to production safely: input/output filtering, PII redaction, content policies, rate and cost controls, DLP for prompts, and monitoring for drift, abuse, and hallucination.
ExploreOperationalize responsible AI: governance frameworks, model inventories, EU AI Act readiness, NIST AI RMF and ISO/IEC 42001 alignment, and board-ready AI risk reporting.
ExploreAutonomous and semi-autonomous agents for security operations - phishing response, vulnerability triage, compliance evidence collection - with human-in-the-loop controls and audit trails.
ExploreIndependent assessment of models and AI vendors: bias and fairness testing, robustness evaluation, supply-chain review of weights and datasets, and safety cases for high-stakes deployments.
ExploreCounter AI-enabled adversaries: deepfake and voice-clone detection, AI-generated phishing defense, executive impersonation protection, and threat intelligence on emerging AI attack tooling.
ExploreTwo decades of offensive security, forensics, and incident response - applied to the newest attack surface on earth.
Every LLM system I build is designed against the same adversary playbook I have run in red team engagements - injection, exfiltration, abuse, and evasion are assumed, not discovered later.
Production LLM systems - RAG, agents, evals, observability - engineered with the same rigor as enterprise security architecture, not notebook prototypes promoted to production.
EU AI Act, NIST AI RMF, ISO/IEC 42001 - compliance mapped from day one, so legal and the board sign off as fast as engineering ships.
From 500+ compromise assessments and 200+ forensic investigations, I know exactly how systems fail in the real world - and I apply those lessons to AI before attackers do.
Active research and development at the frontier of AI and cyber defense - available as advisory, prototyping, and co-innovation engagements.
Self-driving SOC experiments: multi-agent triage, machine-speed containment, and the guardrails that keep autonomy accountable.
Quantum-safe cryptography migration planning for AI pipelines - protecting model weights, embeddings, and data against harvest-now-decrypt-later.
Training environments where defenders spar against AI-driven adversaries - realistic, repeatable, and calibrated to your threat model.
Simulating attacks against digital twins of critical infrastructure before adversaries rehearse on the real thing.
The questions every serious buyer of AI work should ask - answered up front.
No. Client data is never used to train external models. Engagements use enterprise API agreements with zero-retention options or fully private deployments in your environment. Data handling, residency, and retention are defined in writing before any system touches your data.
Model-agnostic by design - frontier APIs (Claude, GPT, Gemini) and open-weight models (Llama, Mistral) that run in your own infrastructure. The choice is driven by your data sensitivity, latency, cost, and compliance requirements, not by vendor preference.
Yes. For regulated and high-sensitivity environments I design private deployments - your cloud tenancy, on-premises GPU infrastructure, or air-gapped setups - so prompts, documents, and outputs never leave your control.
A working prototype typically lands in two to six weeks. Production systems follow a phased path - discovery, prototype, evaluation, security hardening, deployment - so you see real results early and invest further only on evidence.
Defense in depth: threat modeling before design, least-privilege tool access for agents, input/output guardrails, PII redaction, and adversarial red teaming against the OWASP LLM Top 10 - then continuous evals and monitoring after launch, because model behavior drifts.
Especially then. Employee AI use is where most data leakage happens today. Usage policies, data classification rules, vendor assessments, shadow-AI discovery, and EU AI Act applicability checks give you the benefits of AI without the uncontrolled risk.
Whether you are shipping your first LLM feature, hardening a GenAI platform, or bringing AI into your SOC - start with someone who has spent twenty years on both sides of the attack.