How is Secure AI SDLC different from a normal Secure SDLC?

A normal Secure SDLC secures conventional software. Secure AI SDLC adds AI-specific concerns — prompt injection, training/RAG data exposure, model misuse, and unsafe autonomous actions — which require dedicated design and controls.

Do you work with our existing AI/ML team?

Yes. We collaborate with your AI/ML and product engineers, embedding security into your existing design and development process rather than replacing it.

Can you also test the AI system after it is built?

Yes. AI red-teaming and assessment are available under our Assess & Pentest pillar (AI & Intelligent Systems), which pairs naturally with Secure AI SDLC.

What is a typical timeline for Secure AI SDLC?

Engagements run 6–12 weeks: baseline AI architecture review, threat modeling, gate design, and pilot on one AI feature before scaling.

Secure AI SDLC

Design and build secure LLM, RAG, and AI systems from the start with AI-specific engineering controls

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About This Service

Our Secure AI SDLC service helps you design and build secure LLM, RAG, chatbot, and AI-powered systems from the start. Aligned with NIST SP 800-218A, NIST AI RMF, Google SAIF, and the OWASP LLM Top 10, we engineer prompt-injection defenses, RAG data-access controls, output validation, and model and data protections into your AI product before it ships — not after an incident.

Why it matters

LLM, RAG, and agentic features introduce risks traditional SDLC does not cover
Prompt, model, and data boundaries need design-time controls
Shipping AI products without secure AI SDLC creates compliance and safety debt
Post-release pentest alone cannot fix architectural AI flaws

Typical engagement

Duration

6–12 weeks depending on AI product complexity and team maturity

Your involvement

AI/ML team access, architecture docs, sample prompts and data flows

Prerequisites

Description of AI features in scope (LLM, RAG, agents) and release timeline

Part of Secure AI Systems Engineering

Secure AI SDLC covers products you ship; pair with agentic security for autonomous action controls.

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Who Needs This

Teams building chatbots, RAG systems, or LLM-powered features

Product companies integrating LLMs into their offerings

Organizations handling sensitive data in AI workflows

Teams that need to avoid prompt injection and data leakage by design

What's Included

Secure AI architecture and design review

Prompt-injection defense design (direct and indirect)

RAG data-access control and isolation design

LLM output validation and safe-handling strategy

Model, data, and prompt protection controls

AI abuse-case modeling and threat modeling

Alignment with NIST AI RMF, SAIF, and OWASP LLM Top 10

AI red-team readiness guidance

How It Works

AI System Discovery

We map your AI system architecture, data flows, models, prompts, and integrations to define the security perimeter

Threat & Abuse-Case Modeling

We model AI-specific threats — prompt injection, data leakage, unsafe actions — using OWASP LLM Top 10 and MITRE ATLAS

Secure Design & Controls

We design defenses: input/output validation, RAG access control, model and data protections, and guardrails

Validation & Readiness

We validate controls and prepare your system and team for AI red-teaming and ongoing assurance

AI drafts AI threat catalogs; architects validate boundaries

AI does

Drafts first-pass AI threat models from architecture inputs

Expert decides

Security architects refine and validate the threat model

AI does

Suggests prompt-injection and output-validation defenses

Expert decides

Experts decide the control design that fits the product

AI does

Maps design gaps to OWASP LLM Top 10 and AI RMF

Expert decides

Engineers prioritize remediation against business risk

Deliverables

Secure AI architecture and design recommendations
AI threat model and abuse-case catalog
Prompt-injection and output-validation control design
RAG data-access control specification
Model, data, and prompt protection plan
NIST AI RMF / SAIF / OWASP LLM Top 10 alignment map
AI red-team readiness checklist

Measurable outcomes

Secure AI SDLC activities mapped to model, data, and deployment phases
Threat models covering prompt injection, data leakage, and agent abuse
Review gates for AI components before production promotion
Bridge to Assess & Pentest for validation of shipped AI systems

Package Fit

Launch

A secure-design review and threat model for your first AI feature.

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Scale

Full Secure AI SDLC with control design across your AI products.

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Enterprise

AI security operating model, governance, and red-team readiness at scale.

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Why HafezSecure

Standards-Anchored

We design to NIST SP 800-218A, AI RMF, SAIF, and OWASP LLM Top 10 so your AI security is defensible

Secure by Design

We build defenses into the architecture early, which is far cheaper than retrofitting after an incident

AI-Native Expertise

We understand LLM, RAG, and agentic patterns and the unique threats they introduce

Product-Team Friendly

Designs are practical for shipping product teams, balancing security with user experience

Typical results

Product teams establishing Secure AI SDLC typically embed design-review gates and AI-specific threat models before first production LLM features ship.