HafezSecure

AI-Assisted Development Security

Govern Copilot, Cursor, and internal AI assistants—validate AI-generated code and keep human review accountable in every PR.

Govern AI coding tools, validate AI-generated code, and secure AI-assisted workflows.

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What teams tell us

  • Developers use Copilot, Cursor, ChatGPT, or similar without clear rules
  • We do not know what security risks AI-generated code introduces
  • We need policies for AI coding tools—not a blanket ban
  • We want to prevent insecure suggestions while keeping velocity
  • We need human review accountable for AI-assisted merges

Who starts here

Engineering orgs with broad AI coding tool adoption
Security leaders answering auditor questions on AI use
Teams pairing policy with technical validation workflows
Scale buyers adding AI governance to DevSecOps programs

What you gain

  • Acceptable-use and data-handling policy for AI coding tools
  • PR workflows that catch insecure or leaked patterns in AI-assisted code
  • Training for developers on safe prompt and review practices
  • Alignment between AI tool rollout and existing AppSec controls
When to start

Start here when AI coding tools are already in daily use. Policy-only engagements can begin quickly; validation workflows follow once tools are inventoried.

Standards & frameworks

OWASP Top 10

Baseline for reviewing AI-suggested application changes.

Secure coding practices

Human-in-the-loop validation beyond tool output trust.

AI governance

Policies for IP, licensing, secrets, and training data boundaries.

Services in this capability

Explore individual offerings in this family.

How we engage

1

Tool & risk inventory

Document which AI assistants are in use and what data they can access.

2

Policy & guardrails

Publish AI coding policy and integrate checks into IDE/PR flows.

3

Validate in delivery

Extend code review and CI rules for AI-generated diffs.

4

Coach & measure

Developer workshops and metrics on policy adherence.

Package fit

AI coding scope is a key scoping parameter—Launch may cover policy; Scale adds validation workflows and training.

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Frequently asked questions

Is banning AI coding tools the only safe option?

No. Most organizations need governed use: clear data rules, approved tools, mandatory review, and automated checks—not a blanket ban that pushes shadow AI use.

How does this relate to Secure AI SDLC?

This family secures how developers use AI to write code. Secure AI Systems Engineering covers products you ship that embed LLMs, RAG, or agents.

Is banning AI coding tools the only safe option?

No. Governed use—approved tools, data rules, mandatory review, and automated checks—beats bans that drive shadow AI.

Related capabilities

Secure Engineering Foundations
Secure SDLC, architecture review, threat modeling, code review, and developer enablement.
DevSecOps & Release Security
Security automation and release controls integrated into CI/CD and supply chains.
Secure AI Systems Engineering
Secure AI SDLC, LLM and RAG design, model and data protections, and agentic controls.
Continuous Secure Engineering
Security champions, continuous AppSec, governance, and maturity programs.

Not sure which package fits your team?

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