How I build and secure AI
Security first.
Most teams can demo AI. Almost nobody can put an agent in production they can actually trust. That gap is what I close. I sandbox what an agent can run, control what it can reach, and write every action to a tamper-evident record — so what your agents do stays contained, attributable, and provable after the fact. No business should have to choose between using AI and protecting what it holds.
Sandboxed execution
Agents run with bounded, deny-by-default permissions. Dangerous operations route through a single supervised boundary that fails closed: what an agent wasn't cleared to do, it can't do.
Controlled egress
An agent cannot reach anything you have not explicitly allowed. Data does not quietly leave.
Signed audit
Every action lands in a tamper-evident audit trail. If anyone alters the record, verification fails. You can prove what your AI did, after the fact, to anyone.
The substrate
The Polis Stack.
I don't sit on top of someone else's AI infrastructure. I built my own, end to end: a verified language, a supervised runtime, and a reasoning engine that shows its work.
Verifies.
Logos
A security-typed programming language, designed so entire categories of security bugs cannot exist by construction: a compiler that rejects insecure code outright instead of letting it fail in production.
Supervises.
Diogenes
The runtime that wraps every AI agent I ship — the watchman that catches an agent the moment it tries to do something it shouldn't, before anything reaches your systems.
Reasons.
Heraclitus
A reasoning engine for tax and accounting work, grounded in the Internal Revenue Code and the CFR so answers come from the source text rather than a model's memory, with a deterministic verifier designed to re-derive claims instead of trusting them.
Off-the-shelf agent frameworks fail in predictable ways. Prompt injection slips through, audit logs get edited, agents call tools they shouldn't. The Polis Stack is my answer: own the substrate, verify the language, supervise the runtime, make claims re-derivable.
Regulated environments
The version your compliance team can say yes to.
AI inside a bank has to clear a higher bar: regulators, model risk teams, and auditors all need to know what it did and why. That's the bar I build to by default. If your team wants AI and your compliance department keeps saying no, this is the version they can say yes to.
- 01
Deploys inside your perimeter — your hardware or your private cloud — with fully local model options where data cannot leave the building.
- 02
A human approves anything irreversible. Your risk and audit teams can verify the trail independently.
I work with public-sector and financial teams on cybersecurity and AI assurance, from full builds to focused engagements. Repositories and published research available on request. Security contact: security.txt