Traditional Secure SDLC vs AI / LLM Security SDLC
This page outlines the key differences between a Traditional Secure Software Development Lifecycle (SDLC) and a modern AI / LLM Security SDLC, including phases, security checks, and governance considerations required to safely deploy AI-enabled systems at scale.
Overview
| Dimension | Traditional Secure SDLC | AI / LLM Security SDLC |
|---|---|---|
| Primary Asset | Application code | Data, prompts, models, agents |
| Core Risk | Software vulnerabilities | Unsafe decisions, hallucinations, data leakage |
| Security Focus | Code & infrastructure | Model behavior & autonomy |
| Failure Impact | System compromise | Business, legal, and trust failure |
| Security Model | Mostly static | Continuous and adaptive |
Key Shift:
Traditional SDLC secures systems.
AI/LLM SDLC secures decisions.
Phase-by-Phase Comparison
1. Strategy & Requirements
Traditional Secure SDLC
- Business and functional requirements
- Security requirements (CIA triad)
- Compliance mapping (e.g., NIST 800-53, ISO 27001)
AI / LLM Security SDLC
- AI use-case definition (what AI is allowed and not allowed to do)
- Risk classification of AI-enabled decisions
- Framework alignment:
- NIST AI Risk Management Framework (AI RMF)
- OWASP LLM Top 10
AI-Specific Checks
- Is AI required, or can deterministic automation suffice?
- What decisions can AI influence?
- Is human-in-the-loop required?
2. Design & Architecture
Traditional Secure SDLC
- Threat modeling (STRIDE)
- Secure application architecture
- Authentication and authorization design
AI / LLM Security SDLC
- AI threat modeling (prompt injection, indirect injection, data poisoning)
- Model, data, and agent architecture
- Agent permissions and blast-radius containment
AI-Specific Checks
- Prompt trust boundaries
- RAG data isolation and freshness
- Agent action scope limitations
3. Development & Build
Traditional Secure SDLC
- Secure coding standards
- Dependency management
- Secrets management
AI / LLM Security SDLC
- Secure prompt engineering
- Model and dataset provenance tracking
- Separation of training, inference, and operational data
AI-Specific Checks
- Prompt hardening patterns
- Embedding and context sanitization
- Model version traceability
4. Testing & Validation
Traditional Secure SDLC
- SAST, DAST, SCA
- Penetration testing
- Functional testing
AI / LLM Security SDLC
- Adversarial AI testing and red-teaming
- Prompt injection and jailbreak testing
- Behavioral validation (hallucination, bias, unsafe output)
AI-Specific Checks
- Can indirect prompts manipulate behavior?
- Does the model fabricate or overreach?
- Can agents exceed intended authority?
5. Deployment & Release
Traditional Secure SDLC
- Secure CI/CD pipelines
- Environment hardening
- Change management approvals
AI / LLM Security SDLC
- AI pipeline security
- Model endpoint isolation
- Prompt and model change governance
AI-Specific Checks
- Model drift readiness
- Prompt/model rollback strategy
- Runtime policy enforcement
6. Runtime Monitoring & Operations
Traditional Secure SDLC
- Logging, metrics, alerts
- WAF, RASP, SIEM integration
- Incident response playbooks
AI / LLM Security SDLC
- Prompt, response, and agent behavior monitoring
- AI guardrails and LLM firewalls
- AI-specific incident response procedures
AI-Specific Checks
- Are outputs deviating from expected behavior?
- Is sensitive data appearing in responses?
- Are agents invoking unauthorized tools?
7. Governance, Risk & Compliance
Traditional Secure SDLC
- Periodic audits
- Control evidence collection
- Risk reviews
AI / LLM Security SDLC
- Continuous AI governance
- Decision traceability and accountability
- Ethical and regulatory alignment
AI-Specific Checks
- Who owns AI-driven outcomes?
- Can decisions be explained and audited?
- Are models aligned with evolving regulations?
AI / LLM Security Program Leadership (Example)
Program Responsibilities
- Defined enterprise AI/LLM security use cases across the full AI lifecycle
- Mapped AI risks to NIST AI RMF and OWASP LLM Top 10
- Evaluated vendor tooling to assess coverage and gaps
- Led proofs-of-concept (POCs) to validate controls before enterprise rollout
- Operationalized AI security controls across cloud, CI/CD, and AI platforms
Resume Summary Example
AI/LLM Security Program Lead driving enterprise use-case definition, framework-aligned risk coverage, and POC execution to operationalize secure AI adoption across the organization.
Key Takeaway
Traditional Secure SDLC focuses on protecting applications.
AI / LLM Security SDLC focuses on protecting decisions, behavior, and trust.
Organizations that treat AI like traditional software inherit unmanaged risk.
Organizations that adopt an AI-specific SDLC build safe, scalable, and durable AI systems.