AI can write your infrastructure. It can't decide what it should be.

Bringing AI into your DevSecOps practice

Decisions we make togetherWhich work AI takes on, where people decide, which checks are hard stops, which tools may touch which code.

Most teams bought AI coding tools and expected speed. It doesn't arrive on its own. In a small controlled study, experienced developers were 19% slower with AI while believing they were faster.1 Google's DORA research found AI amplifies whatever a team already has.2

We measure where your delivery time goes, then redesign the workflow. AI drafts, reviews first and triages scanner noise. Automated checks are hard stops, and people decide what ships. Your standards are written into the repositories so the AI works your way.

Measured by lead time, change failure rate, review time and issues caught before production, before and after.

Cloud foundations that fit how you're organised

Decisions we make togetherAccount structure and ownership, IP address plan, identity model, networking, logging and retention, how exceptions get approved.

Any capable team can now generate a working AWS landing zone in a week. The costly mistakes are in its shape: accounts that don't match who is accountable, a network that can't absorb the next merger or machinery-of-government change, guardrails so tight that teams route around them.

We work through those decisions with your architects, security and finance people, and record each with the options rejected. The build is AI-assisted and reviewed line by line, so most of the budget goes on the shape.

Measured by time to a new compliant account, share of workloads inside guardrails, and no rebuild when you grow.

Saying yes to AI, safely

Decisions we make togetherWhich data may go to which AI tools, who approves a new use case and on what evidence, how much an agent may do alone, what happens when one misbehaves.

AI brings new risks: sensitive data pasted into public tools, agents with more access than intended, models talked into misbehaving, no record of who approved what. Blocking AI doesn't work; staff use it anyway.

Scanners and pipelines are the plumbing. What we build is an approval path your organisation trusts: data rules people can follow, a hard limit on what each agent can reach, attacker-style testing, and a plan for when something goes wrong. For NSW agencies, it produces the evidence the AI Assessment Framework asks for.

Measured by time to approve a new use case, unapproved AI tools in use, and reviews passed first time.

A security review that ends in fixes

Decisions we make togetherWhich risks to fix now, which to accept, and who owns them.

Security tools produce hundreds of findings and no way to tell which five matter.

AI groups and de-duplicates the findings in hours. The time saved goes on tracing real attack paths through identity and network, and fixing the worst of them with you.

Measured by critical attack paths closed within the engagement, not pages of report.

Cloud and AI spend that someone owns

Decisions we make togetherWho owns each cost, what a unit of your business should cost to run, which AI model is worth paying for on which task.

AI is now the fastest-growing and least governed line on many cloud bills. One runaway agent loop or oversized model can cost more in a month than its server does in a year.

AI works through your billing and tagging data in days. The time goes on what lasts: architecture that costs less by design, the right model for each task, and budgets with a named owner.

Measured by cost per transaction, customer or case, and the share of spend with an owner.