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01/08/2026 8:59 pm
TL;DR: Security tools still arrive late in the software lifecycle, evaluating artifacts after intent has already hardened into code, according to Seezo. In an AI SDLC built around coding agents, the real opportunity is workflow continuity: security context, deeper review, and implementation validation need to follow the work rather than interrupt it.
NHIMG editorial — based on content published by Seezo: What developer-first security looks like in the AI SDLC
Questions worth separating out
Q: How should security teams control AI-assisted coding without slowing developers down?
A: Put policy into the IDE so security guidance appears during code creation, not after commit.
Q: Why do late-stage code scans miss the real security problem in the AI SDLC?
A: Late-stage scans inspect artifacts after the design decision is already made, so they often catch consequences rather than influence choices.
Q: What do teams get wrong about security inside coding agents?
A: They often assume placing a scanner or policy bot inside the agent makes the experience developer-first.
Practitioner guidance
- Implement context-aware security delivery Deliver authentication, gateway, and rate-limiting requirements only when the coding task makes them relevant, so developers do not have to request security guidance in a separate workflow.
- Persist review outcomes beyond the session Store deeper assessment results as durable requirements that can be reused during implementation and validated again in CI or pull request review.
- Unify agent, CI, and pull-request checks Make the same security decision visible in the coding harness, the asynchronous assessment flow, and the pull request so developers never have to translate findings between tools.
What's in the full article
Seezo's full blog covers the operational detail this post intentionally leaves for the source:
- How the coding harness, terminal, pull request, and CI surfaces are intended to work as one connected workflow.
- How persistent security requirements are stored and reused across design review, implementation, and validation.
- How asynchronous assessments return results back into the same developer session without forcing a second tool.
- How the platform is intended to balance local developer speed with governance visibility for security teams.
👉 Read Seezo's blog on developer-first security in the AI SDLC →
AI SDLC security in coding agents: what teams should change now?
Explore further
01/08/2026 9:06 pm
Developer-first security is a workflow governance problem, not a scanner problem. The article is correct that moving the same disconnected process into an agent does not fix the underlying issue. Security value depends on when context arrives, not just where the tool sits. For practitioners, that means the real control is continuity of decision support across design, implementation, and review, not another checkpoint bolted onto the pipeline.
A question worth separating out:
Q: Who is accountable when an AI coding tool introduces insecure code into production?
A: Accountability stays with the organisation that allowed the tool to operate without enforced guardrails. The model is not the accountable party. Security, engineering, and platform owners share responsibility for defining policy, enforcing it at runtime, and logging the decision trail that proves controls were applied.
👉 Read our full editorial: Developer-first security in the AI SDLC needs workflow continuity
