Secure AI Systems Engineering

Build and ship AI products safely—Secure AI SDLC, LLM/RAG design, model and data controls, and agentic security engineering.

Secure AI SDLC, LLM and RAG design, model and data protections, and agentic controls.

What teams tell us

  • We are shipping LLM, RAG, or agent features without secure AI SDLC
  • Prompt injection and data leakage risks are unclear at design time
  • Agents can call tools—we need permission and approval models
  • We need design gates before AI components reach production
  • Assessment after launch cannot fix architectural AI flaws

Who starts here

Product teams building AI-powered features
Organizations deploying agents with MCP or tool-calling
Enterprise buyers needing secure AI SDLC and agentic controls
Teams separating developer AI tools from shipped AI products

What you gain

  • Security requirements woven into AI feature design and release
  • Controls for prompts, tools, data stores, and model endpoints
  • Agent workflows reviewed for abuse cases and privilege boundaries
  • Evidence for auditors and customers on AI risk management
When to start

Start here when AI is in the product you ship—not just in how developers write code. Enterprise packages often include this family.

Standards & frameworks

Risk catalog for LLM applications and integrations.

OWASP Agentic Top 10

Security patterns for autonomous and tool-using agents.

Risk management framing for AI systems in production.

Adversarial tactics for machine learning systems.

How we engage

1

Scope AI surfaces

Identify LLM features, RAG pipelines, agents, and data flows in scope.

2

Threat model & requirements

Apply LLM/Agentic Top 10 and ATLAS-informed abuse cases.

3

Engineer controls

Implement guardrails, monitoring, and secure SDLC gates for AI releases.

4

Validate & operate

Red-team style tests and runbooks for model and agent incidents.

Package fit

Enterprise engagements often combine Secure AI SDLC with agentic security when shipping customer-facing AI.

View Build Secure packages

Frequently asked questions

We only use AI for internal coding—is this family still relevant?

That use case fits AI-Assisted Development Security. This family applies when you ship LLM features, RAG, or agents to users or integrate models into production products.

Do you assess our models like a pentest?

We engineer secure AI delivery and can align with Assessment services for LLM/agent testing. Build Secure focuses on how you build and operate AI systems.

Can you test the AI system after it is built?

Yes. Secure AI SDLC is design-time; we cross-link to Assess & Pentest services for LLM, RAG, and agent validation before and after launch.

Not sure which package fits your team?

Book a Build Secure Workshop