What is the difference?
SSDLC means Secure Software Development Life Cycle: security work is part of requirements, design, implementation, testing, deployment, and maintenance. SAIDLC, as proposed in my Secure AI-Driven Development Life Cycle article, applies that foundation to development where AI assistants and agents take an active role.
The difference is the scope of the security review. Alongside the application, the team examines what its AI tools can read, change, execute, and send outside the environment. SAIDLC is a proposed model for organizing that work, not a separate compliance standard.
Keep the secure development foundation
Teams still need security requirements, threat modeling, code review, dependency checks, and a response process for vulnerabilities. These activities do not disappear when an assistant writes the code. NIST's Secure Software Development Framework describes practices that can be integrated into different development lifecycles; it does not define or endorse SAIDLC.
A useful starting point is the existing release process. Identify where AI changes the trust boundaries, then add controls there. Replacing a working security program with a new acronym would create paperwork without necessarily improving the software.
Compare the decisions teams need to make
| Decision | SSDLC foundation | AI-specific consideration in SAIDLC |
|---|---|---|
| Threat modeling | Application assets and trust boundaries | Also review prompts, retrieved context, tools, and agent access |
| Implementation | Secure coding and peer review | Apply those checks to generated changes and restrict tool execution |
| Verification | Check requirements and security behavior | Do not use the generating agent's own assessment as the only evidence |
| Release | Controlled builds and deployment permissions | Separate an agent's ability to prepare a release from authority to deploy it |
SSDLC can already include these controls. The proposed SAIDLC label makes the AI-related responsibilities explicit; it does not mean SSDLC prevents teams from addressing them.
Example: an assistant changes an employee API
Suppose an assistant adds an endpoint for employee records. The usual review asks whether the endpoint validates input and enforces access to each record. The AI workflow adds questions: Did the assistant receive real personnel data? Could it read production credentials? Did an external document influence a shell command?
A practical response is to use synthetic records, give the assistant only the repository access it needs, and test access denial with a second user's identity. A human reviewer checks the authorization path before merge. These steps address both the resulting code and the process that produced it.
Add controls where AI changes the risk
Inventory the AI tools in use, document their permissions, and assign an owner to each sensitive workflow. Start with high-impact operations such as authentication changes, bulk exports, and production deployment. Keep evidence in the same pull requests and release records the team already uses.
For a practical checklist, read How to Secure AI-Assisted Software Development. OWASP's AI Agent Security Cheat Sheet provides further guidance on limiting tool privileges and protecting data.
I build web apps, APIs, and internal systems with security in mind from the start.
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