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02/08/2026 11:47 am
TL;DR: AI is accelerating code velocity, complexity, and exploit discovery faster than human security teams can respond, making proof of trust more important than counting scans, according to Veracode. The shift moves application security toward continuous risk reduction, governed AI-assisted development, and evidence that software is safe to ship.
NHIMG editorial — based on content published by Veracode: The Mythos Moment: Why the Future of Cybersecurity Is Software Trust
Questions worth separating out
Q: How do security teams prove software is trustworthy to auditors and boards?
A: They need evidence beyond scanner output: approved provenance, continuous verification, remediation timelines, and enforceable policy gates.
Q: Why does AI-assisted development complicate application security governance?
A: AI-assisted development complicates governance because the organisation must track who authorised the change, what system generated it, and whether the output can be audited.
Q: What do security teams get wrong about scanning code faster?
A: They often assume more scanning will close the gap created by faster development.
Practitioner guidance
- Define release trust thresholds Set explicit criteria for what must be true before software can ship, including reachability, exploitability, dependency criticality, and evidence of approval for AI-assisted changes.
- Tie AI-assisted changes to accountable ownership Record which human approved the change, which system generated it, and what policy gate allowed it through the pipeline so the release can be audited end to end.
- Reduce exposure before deployment Prioritise controls that stop or delay material risk before release, rather than relying on post-release backlog triage to close the gap.
What's in the full article
Veracode's full article covers the analytical detail this post intentionally leaves for the source:
- Its specific interpretation of how AI is compressing exploit windows across modern software delivery.
- The article's full discussion of software trust as a new security category and control plane.
- The author’s framing of how boards and executives should think about provable release confidence.
- The source article's direct commentary on AI-assisted development and what it changes for application security teams.
👉 Read Veracode's analysis of software trust in the AI era →
Software trust in the AI era: are your release controls keeping up?
Explore further
02/08/2026 12:57 pm
Software trust is the right category for AI-era application security. The article correctly identifies that scanning volume no longer equals security outcome when code velocity and exploit velocity both rise. In that environment, trust becomes the organising principle because teams need to answer whether a release is materially safe, not merely whether it was inspected. For practitioners, the governance shift is from finding more issues to proving which issues matter.
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: Software trust is becoming the new control plane for AI-era risk
