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title: Gen-AI Across the Secure Software Development Lifecycle
description: A phase-by-phase guide to integrating generative AI into a Secure Software Development Lifecycle (SSDLC) without bypassing security controls. Covers all six...
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# Gen-AI Across the Secure Software Development Lifecycle

**[gitconnected](https://daily.dev/sources/gc)** · 14 min read · 0 upvotes · 0 comments

## Summary

A phase-by-phase guide to integrating generative AI into a Secure Software Development Lifecycle (SSDLC) without bypassing security controls. Covers all six phases — Requirements, Design, Implementation, Verification, Release, and Operations — detailing where AI accelerates work (requirements gap analysis catching 60–70% of gaps, threat modeling improving STRIDE coverage from 67% to 94%, incident triage cutting mean time to triage by 65%), where guardrails are essential (AI-generated code producing 40% more vulnerabilities per a 2025 Stanford study without SAST), and what controls must remain human-owned (security sign-off, architectural decisions, compliance attestation). Includes a 10-sprint implementation roadmap and concrete tooling recommendations for each phase.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://levelup.gitconnected.com/gen-ai-across-the-secure-software-development-lifecycle-78db31052852>

## Questions this post answers

### How much faster does AI-assisted threat modeling make security review sessions?

A financial services company tracked threat modeling over six months and found session duration dropped from 3.5 hours to 2 hours (a 43% reduction), while threats identified per session rose from 123 to 1 to 158%, and STRIDE category coverage improved from 67% to 94%. Post-release security findings also dropped from 8.2 to 3.1 per quarter, a 62% decrease.

_Teams weighing AI-assisted threat modeling can track real adoption results like these on daily.dev._

### Do AI coding assistants introduce more security vulnerabilities than manual coding?

Yes, a Stanford study found developers using AI coding assistants produced code with 40% more security vulnerabilities compared to manual coding, though those same developers worked 55% faster. The net effect on security depends on guardrails: with SAST integration and security-focused review, AI-assisted development can be both faster and more secure; without them, it becomes faster and more vulnerable.

_Developers deciding how to guardrail AI coding tools can follow this security tradeoff research on daily.dev._

### Can AI-generated security tests be trusted without human review?

No. A team added 200 AI-generated security tests that all passed, giving false confidence, but six months later a penetration test found SQL injection in an endpoint that had 12 AI-generated tests covering it. Those tests only checked that the endpoint returned HTTP 200 for valid input rather than actually testing for injection, so AI-generated security tests require human review before being added to a CI suite.

_Anyone building AI-generated test suites can weigh this false-confidence risk via daily.dev before trusting CI gates._

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---

Tags: [#security](https://daily.dev/tags/security), [#ai-coding](https://daily.dev/tags/ai-coding), [#devsecops](https://daily.dev/tags/devsecops)

[View this post on daily.dev](https://daily.dev/posts/gen-ai-across-the-secure-software-development-lifecycle-p80glohxi)

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