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Services Secure AI SDLC & Engineering

Secure AI SDLC & Engineering

Let AI write your code — and trust every merge.

Copilots and coding agents are already in your repos. We build the guardrails that make AI-assisted engineering safe to scale — sandboxed agents, protected secrets and branches, review gates tuned for machine-written code — so you scale the velocity and keep a codebase you trust completely.

5.0★Gartner Peer Insights · 4.8★G2

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Tell us which AI tools your engineers use. We'll come back with the top risks in how they're wired — and the guardrails we'd add first.

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What is a secure AI SDLC?

A secure AI SDLC is your software development lifecycle re-engineered for AI-generated code and autonomous coding agents. It answers, in controls rather than vibes: which AI tools are sanctioned and how they're configured; what agents can read, execute, and merge; how machine-written changes get reviewed, tested, and attributed; and how secrets, CI, and production stay out of an agent's reach. With it, every AI-assisted change lands reviewed, tested, and attributed — and agents hold exactly the access their task needs.

What you get

01

Sanctioned tools, configured right

Enterprise modes on, training on your code off, IP and retention settings verified — for Copilot, Cursor, Claude, and whatever your engineers adopt next. The difference between safe and exposed is usually configuration.

02

Agents with exactly enough access

Coding agents run in isolated environments with scoped, short-lived credentials — production keys and CI secrets stay yours, and every path to main goes through review. Scope is a design decision made up front, so agents can run fast, safely.

03

Review gates built for machine-written code

AI-authored changes labeled for provenance, review depth scaled to risk, and CI gates — tests, SAST, secret scanning — that hold regardless of who or what wrote the diff.

04

A policy engineers embrace

Short, concrete rules for what code and data can go into which tools, written with your team. Paved roads win because they're the fastest safe path.

How it works

  1. 1

    Assess

    Typically 1–2 weeks.

    We map how AI is actually used across your engineering org — tools, configurations, agent permissions, review coverage — and rank the opportunities to harden by impact.

  2. 2

    Harden

    A few weeks, sized to your gaps.

    Tool configurations fixed, agent sandboxes and scoped credentials stood up, CI gates wired, and the AI engineering policy written with your team. Your engineers feel it as a handful of PRs, not a lost quarter.

  3. 3

    Operate

    Ongoing, as the tools evolve.

    The toolchain changes monthly; the guardrails have to keep up. We review new tools as your team adopts them, drill the agent-incident playbook, and keep the policy matched to reality.

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FAQs

Secure AI SDLC & Engineering questions, answered

Will guardrails slow our engineers down?
Done right, they speed things up: engineers stop improvising per-tool judgment calls and get pre-approved tools with safe defaults and a fast lane for new ones — the quickest path there is.
Is AI-generated code actually less secure?
It's secure the same way human code is: through review, tests, and gates. AI simply raises the volume, so the structure matters more — provenance labeling, risk-scaled review, and CI gates that hold regardless of who wrote the diff. With those in place, AI-written code clears the same bar as everything else.
Do you cover autonomous coding agents, or just autocomplete?
Both — agents are where the program shines. An assistant suggests code a human commits; an agent holds credentials and acts. We sandbox execution, scope tokens to the task, keep production and CI secrets out of reach, and make sure nothing merges without the same review a human's PR would get.
What about our IP — is our code training someone's model?
That's a configuration and contract question with a concrete answer per tool. We verify retention and training settings at the enterprise tier, align the contractual terms, and put provenance records on AI-assisted changes so you can answer the question confidently in diligence.
How does this connect to compliance?
SOC 2 change-management controls assume humans write code; auditors are starting to ask how AI-written code is reviewed. This program produces the policy and evidence that answer cleanly — and if you're pursuing ISO 42001, it slots straight into the AIMS. Billed like all our work: 15-minute increments, optional monthly cap.
Related services
Secure MCP Program MCP connects your AI to everything. We make every connection one you can trust. Shadow-AI Detection & Mitigation Find every AI tool your company already uses — and make each one safe to keep. Product Security The security features your enterprise buyers ask for — shipped by us, in your codebase.
Give your agents exactly what they need — then let them run.

Ship faster — and still sleep

Send your company email and we'll come back with your AI engineering risk review.

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5.0★Gartner Peer Insights · 4.8★G2