AI Security
Govern AI before it governs your risk exposure.
Organizations are deploying LLMs, AI agents, and ML models faster than their security and governance programs can adapt. The governance gap — knowing what AI you have, who approved it, what data it touches, and what decisions it influences — is more urgent than the technical security controls.
Deep Layer builds AI governance programs and secure AI architectures that address both sides: the organizational controls (policies, risk frameworks, approval workflows, third-party assessment) and the technical controls (prompt injection defense, RAG access control, agent authorization, model supply chain verification). Aligned to ISO 42001, NIST AI RMF, and EU AI Act requirements.
Challenges We Address
The problems that bring clients to us.
No AI Inventory
No visibility into what AI tools employees are using, what data they are feeding into them, or what business decisions are influenced by AI outputs.
Governance Before Controls
Organizations reach for technical controls (prompt injection filters, output validation) before establishing the governance foundation (policies, risk framework, approval workflows).
Model Supply Chain Risk
Open-source models, pre-trained embeddings, and third-party plugins adopted without provenance verification or security assessment.
Agent Authorization Gaps
AI agents with tool-calling capabilities (database access, email, code execution) deployed without scoped authorization frameworks or audit trails.
Regulatory Uncertainty
EU AI Act, state-level legislation, and industry-specific requirements are evolving. Organizations lack the classification and documentation infrastructure to demonstrate compliance.
Ideal Clients
Who this is built for.
Service Offerings
What we deliver.
AI Governance Program Build
Program DevelopmentEnd-to-end governance program structured around five pillars: Policy & Standards, Risk Management, Governance Operations, Third-Party AI Security, and Ethics & Responsible AI.
Secure AI Architecture & Threat Modeling
Design & ArchitectureTechnical security architecture for LLM applications — reference architectures, AI-specific threat modeling (STRIDE applied to AI trust boundaries), agent authorization, and runtime guardrails.
AI Security Readiness Assessment
AssessmentCurrent-state evaluation of AI security posture — inventory of AI systems, risk assessment, governance maturity scoring, and prioritized improvement roadmap.
AI Red Team & Threat Assessment
AssessmentAdversarial testing of AI systems — prompt injection, data exfiltration, jailbreaking, tool abuse, and boundary testing against defined threat scenarios.
MLOps / LLMOps Security
Design & ArchitectureSecurity for the ML pipeline — training data protection, model registry governance, deployment pipeline security, and inference endpoint hardening.
LLM Application Security Assessment
AssessmentAdversarial testing of LLM-powered applications — prompt injection, jailbreaking, data extraction, insecure output handling, and trust boundary failures. Every finding demonstrated with a proof-of-concept interaction sequence.
Agentic AI Security Review
AssessmentSecurity assessment of multi-agent AI systems — tool authorization, inter-agent trust boundaries, memory system security, human oversight mechanisms, and privilege escalation across agent chains.
RAG Pipeline Security Assessment
AssessmentSecurity assessment of retrieval-augmented generation pipelines — vector store access control, ingestion pipeline security, indirect prompt injection via retrieved documents, and document corpus integrity.
Frequently Asked Questions
Common questions.
Do we need AI governance if we are only using third-party AI tools (ChatGPT, Copilot)?
Yes. Third-party AI usage creates data protection, intellectual property, and regulatory risks that require governance controls — acceptable use policies, data classification for AI inputs, and vendor assessment.
What is the difference between AI Governance and Secure AI Architecture?
Governance tells you what is allowed — policies, risk frameworks, approval workflows. Secure Architecture tells you how to build it safely — threat models, reference architectures, runtime guardrails. Most organizations need governance first.
Is AI red teaming the same as penetration testing?
Similar in concept but AI-specific in technique. AI red teaming tests for prompt injection, jailbreaking, data exfiltration through conversation, tool abuse, and boundary violations — threat vectors unique to LLM and agent systems.
How do the LLM, Agentic, and RAG assessments relate to each other?
They cover different layers of the same stack. The LLM Application Assessment covers the individual application (prompt injection, output handling). The RAG Pipeline Assessment goes deeper on the retrieval infrastructure (vector store access control, ingestion security, corpus integrity). The Agentic AI Review covers multi-agent systems (inter-agent trust boundaries, tool authorization, memory systems). A RAG-based agent system could warrant all three.
Do these assessments test the underlying model (GPT-4, Claude)?
No. These assess how your application is built on top of the model — not the model provider's safety guardrails. The attack surface is in your system prompts, tool configurations, retrieval pipelines, and output handling, not in the model itself.
Ready to discuss ai security?
30-minute discovery call. We will discuss your environment, your challenges, and whether there is a fit — no sales pitch.