AI / LLM Security
Building guardrails for AI-driven and LLM-integrated systems.
- Capabilities
- 5
- Tools & platforms
- 5
- Discipline
- Security
Secure AI-driven and LLM-integrated platforms by design — threat modeling model interactions, enforcing guardrails, and controlling how applications and APIs access AI services.
- AI threat modeling — prompt injection and data leakage
- LLM guardrails and model-interaction controls
- Secure AI/LLM and third-party integration patterns
- Identity validation and least-privilege model access
- AI governance and data-protection controls
The stack behind the work.
The tools I reach for day to day — with a rough sense of where my depth sits.
OWASP LLM Top 10
AdvancedAI risk framework & threat modeling
Prompt-Injection Testing
AdvancedAdversarial model-interaction testing
Amazon Bedrock
ProficientGuardrails & controlled model access
Azure OpenAI
ProficientSecure LLM integration & access control
API Gateways
AdvancedMediated, least-privilege model access
Projects that put this to work.
Enterprise AppSec Migration
Driving tiered application onboarding into a unified AppSec program with automated CI/CD gating.
IAM Least-Privilege Redesign
Role redesign and policy enforcement program reducing over-privileged access across cloud accounts.
CI/CD Security Automation
Embedded SAST, DAST and SCA gates into shared CI/CD pipelines for automated pre-deployment validation.
AI/LLM Security Guardrails
Secure-by-design review and guardrail program for AI/LLM-integrated services across the platform.
Application Security
Embedding secure-by-design into the SDLC.
SAST
Static analysis that finds flaws in source before it ships.
DAST
Dynamic testing that probes running apps like an attacker.
Cloud Security
Hardening cloud-native estates at enterprise scale.