Secure AI · AI Application Security
Build AI features on a secure foundation
Most AI vulnerabilities live in the glue code: how prompts are assembled, what the model is allowed to call, and what happens to its output. We review that code and its architecture, help you put guardrails in the right places, and give your developers patterns for shipping AI features safely.
Outcomes
What you walk away with
LLM integrations reviewed line by line for injection and data-flow risks
Guardrails and output validation placed where they actually stop attacks
Secrets, API keys and model credentials scoped and protected
Secure AI coding patterns your team can reuse on every new feature
Coverage
What's in scope
What we review
- Prompt construction and context assembly code
- Tool/function-calling handlers and MCP integrations
- Output handling before it reaches browsers, shells or databases
- Tenant isolation in RAG retrieval and vector stores
- Logging, PII handling and data retention for prompts
What we help build
- Input/output guardrail layers
- Least-privilege agent credentials and approval flows
- AI security checks in CI/CD
- Secure AI development guidelines
Deliverables
- Code-level findings with patches
- Guardrail architecture recommendations
- Secure AI development checklist for your stack
Standards & frameworks
- OWASP Top 10 for LLM Applications
- OWASP ASVS
- NIST AI RMF
- ISO/IEC 42001
Ideal for
- Engineering teams building AI features in-house
- AI-native startups moving from prototype to production
- Companies integrating third-party AI APIs into sensitive workflows
FAQ
AI Application Security questions
How does this differ from AI security testing?
Testing attacks the running system from the outside. AI application security works from the inside, reviewing code and architecture. Many teams combine both for the best coverage.
Related
Often combined with
AI & LLM Security Testing
LLM and AI chatbot penetration testing for RAG pipelines, AI agents and MCP servers: prompt injection, data leakage, tool abuse.
AI Threat Modeling
Design-stage threat modeling for LLM apps and agentic systems: data, tools, autonomy and trust.
Threat Modeling
Map how your system can be attacked before code ships, and decide which controls actually matter.
Ready to talk about ai application security?
Tell us what you're building. In a free 30-minute scoping call we'll map your risks and recommend the right engagement, whether you need a pentest, a threat model or an AI security review.