AI Governance Consulting

Scale your agentic ecosystem, not your risks

We architect the zero-trust governance substrate for your agentic AI ecosystem. By treating non-human identities (NHIs) as critical infrastructure, we enforce continuous runtime protection, dynamic least-privilege scoping, and complete cryptographic auditability across your entire attack surface.

Get in touch
We’ll explore your technology goals and challenges
You’ll get expert insights on the best path forward
We’ll outline next steps to bring your solution to life
UNCOUNTED AGENTS?

Every agent identified, scoped, and fully accounted for

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Verified agent identity

Every agent gets its own scoped, expiring identity tied into your existing IAM—not a shared key floating between services. Agents are non-human identities capable of autonomous decision-making, and they need to be treated as first-class ones.

A group of business professionals collaborating in a lush urban park with towering skyscrapers in the background, symbolizing innovation and efficiency. The scene represents machine learning models optimizing business operations, as technology and sustainability converge to enhance productivity and decision-making in a modern corporate environment.

Least-privilege by default for effective AI governance

Each agent gets exactly the access its task requires, enforced per agent and per task in real time rather than written down in a policy document and audited later.

A group of business professionals collaborating in a lush urban park with towering skyscrapers in the background, symbolizing innovation and efficiency. The scene represents machine learning models optimizing business operations, as technology and sustainability converge to enhance productivity and decision-making in a modern corporate environment.

Tested against attacks for risk management

We run prompt-injection defense, output validation, and red-team testing before rollout, then keep testing as agents change. You get the test report signed off before production—the security and safety evidence a risk review actually asks for.

A group of business professionals collaborating in a lush urban park with towering skyscrapers in the background, symbolizing innovation and efficiency. The scene represents machine learning models optimizing business operations, as technology and sustainability converge to enhance productivity and decision-making in a modern corporate environment.

Full audit trail

Every action is logged and tamper-evident for full accountability, mapped to the frameworks you actually answer to.

NIST AI RMF, which guides risk assessment across the lifecycle; the EU AI Act, which creates binding obligations for high-risk AI use cases; and sector-specific requirements across various industries, including healthcare—plus drift detection and a defined retirement path for those cases.

We'll inventory every agent and non-human identity already running.
You'll see where permissions exceed what the task actually requires.
We'll hand you a risk map and a governance roadmap in a week.
Case Studies

Our client impact in action

The identity & exposure audit, published

The audit protocol is the proof: every agent, service account, and non-human identity inventoried, permission sprawl mapped against actual task requirements, and your machine-to-human identity ratio benchmarked against industry data. We publish the method for transparency: a governance standard you can inspect beats one you're asked to trust.

AI call auditor automates 99% of reviews

A regulatory compliance firm partnered with FullStack to build an AI system that reviews calls for potential SEC violations. The tool scores accuracy and confidence, reducing human review to just 1% of transcripts and saving an estimated 5,500 labor hours and $232,000 annually.

We routed our own AI stack

FullStack is building and running its own gateway across internal AI usage on Connect and Labs tooling, and will publish the real numbers: cost reduction, quality retention, latency, and failover uptime through actual provider outages.

testimonials

What our clients are saying

FullStack’s deep understanding of BenjaminWest’s needs, coupled with consistent updates, made the collaboration seamless and the outcome outstanding.
Joe Eikelberner, COO
BenjaminWest
FullStack acted as true partners and advisors. The expertise around AI and the level of developers, engineers—whatever role it was that came to the table—was just phenomenal.
Marisa Kopec, CEO
Lux Research
Speed is only the byproduct; the real value is better software and better use of our people.
Raj Tatta, VP of Engineering
Paciolan
FullStack turned our vision for The Launchpad into reality. Their intuitive design approach delivered an app that provides IT buyers a seamless and hassle-free experience, effortlessly connecting them with the ideal tech vendors.
Tonya Turrell, Founder & CEO
Technology Match
FullStack completely transformed our company's app, breathing new life into how we service our customer base. Their innovative and collaborative team delivered an application experience that we're proud to have in the market!
Jay Williams, Software Manager
Green Mountain Power
FullStack’s deep understanding of BenjaminWest’s needs, coupled with consistent updates, made the collaboration seamless and the outcome outstanding.
Joe Eikelberner, COO
BenjaminWest
FullStack acted as true partners and advisors. The expertise around AI and the level of developers, engineers—whatever role it was that came to the table—was just phenomenal.
Marisa Kopec, CEO
Lux Research
Speed is only the byproduct; the real value is better software and better use of our people.
Raj Tatta, VP of Engineering
Paciolan
FullStack turned our vision for The Launchpad into reality. Their intuitive design approach delivered an app that provides IT buyers a seamless and hassle-free experience, effortlessly connecting them with the ideal tech vendors.
Tonya Turrell, Founder & CEO
Technology Match
FullStack completely transformed our company's app, breathing new life into how we service our customer base. Their innovative and collaborative team delivered an application experience that we're proud to have in the market!
Jay Williams, Software Manager
Green Mountain Power
MOVING SAFELY

Find out what you're already running

Every governance engagement opens with a one-week Identity & Exposure Audit: every agent, service account, and non-human identity in your environment, and what each one can actually reach.
A group of business professionals collaborating in a lush urban park with towering skyscrapers in the background, symbolizing innovation and efficiency. The scene represents machine learning models optimizing business operations, as technology and sustainability converge to enhance productivity and decision-making in a modern corporate environment.

A real inventory in one week*

We map permission sprawl against actual task requirements and benchmark your machine-to-human identity ratio before designing anything new.

A group of business professionals collaborating in a lush urban park with towering skyscrapers in the background, symbolizing innovation and efficiency. The scene represents machine learning models optimizing business operations, as technology and sustainability converge to enhance productivity and decision-making in a modern corporate environment.

Over-permissioned is the starting condition

This isn't a failure of your team. It's how nearly every agent estate begins, because agents get built to work first and scoped second. The audit tells you how far that has gone.

A group of business professionals collaborating in a lush urban park with towering skyscrapers in the background, symbolizing innovation and efficiency. The scene represents machine learning models optimizing business operations, as technology and sustainability converge to enhance productivity and decision-making in a modern corporate environment.

Governance at build time, not audit time

Identity and policy get attached at the point an agent is created. A compliance gate after the fact rarely catches everything in time—and by the time risk review is asking questions, the agent is already scoped to answer them.

A group of business professionals collaborating in a lush urban park with towering skyscrapers in the background, symbolizing innovation and efficiency. The scene represents machine learning models optimizing business operations, as technology and sustainability converge to enhance productivity and decision-making in a modern corporate environment.

Consistent across platforms, not per platform

AWS, GCP, and Databricks each ship native agent controls. They're real, and they only cover their own platform. Our layer is what keeps enforcement consistent across all of them.

*These are typical time estimates and actual times may differ based on project complexity and scope.
COMPREHENSIVE SOLUTIONS

Explore FullStack's agent governance services

Gartner expects task-specific agents inside 40% of enterprise applications by the end of 2026, up from under 5% in 2025, and projects the average large enterprise will run more than 150,000 agents by 2028. Every one of those is an identity organizations have to account for.
  • Non-human identity inventory and registry
  • Identity and exposure audit
  • Tamper-evident audit logging and compliance mapping
  • Prompt-injection defense and red-team testing
  • Real-time least-privilege enforcement
  • Machine-to-human ratio benchmarking
  • Policy definition, versioning, and continuous improvement

Partner with FullStack and keep every agent accountable

Enterprise Partnerships

One governance layer, every platform

For estates where agents run across AWS, GCP, and Databricks, we build the layer that gives you one real-time inventory, one enforcement standard, and one point of oversight instead of a separate picture per cloud.
Mid-Market Solutions

Governance that doesn't become the bottleneck

For teams where security and risk management review has become the thing that slows AI initiatives down, we replace the bespoke review per use case with one identity and policy substrate aligned to enterprise standards, including ISO/IEC 42001, the first international standard for AI governance.
our blog

Featured articles

People discuss building an ethical AI governance framework.

AI Governance Framework for Scalable, Ethical Production AI

Learn how to implement AI governance frameworks, tools, and responsible practices to ensure ethical, scalable, and compliant AI systems in your organization.
Read post
NVIDIA branding representing the company's open AI models initiative.

What NVIDIA's AI Alliance means for security teams

How NVIDIA’s Open Secure AI Alliance reshapes enterprise AI risk—and what security teams should change in their governance and controls.
Read post
A professional looks intently at her laptop screen while working at a desk with VR headsets nearby, suggesting focused analysis of a tech or AI-related issue.

Why AI goes rogue, and what your team can do about it

Learn why AI agents go off-script and how to design goals, containment, and guardrails that keep systems safe in production.
Read post
Business analyst reviews performance dashboards on dual monitors, illustrating human oversight and risk-based approval of AI agent actions.

Gate Fatigue: When Human Approval Stops Meaning Anything

Gate fatigue can weaken human oversight in AI workflows. Learn how to design approval gates that hold up as agent activity scales.
Read post
Speaker presents on AI governance, highlighting operational controls, monitoring, and responsible AI agent deployment.

How AI Agent Governance Is Moving Into Practice

AI governance is changing fast. Learn how businesses can manage AI agents, access controls, monitoring, and incident response.
Read post
Business team meets in a conference room as a presenter discusses a data chart, illustrating cross-functional planning for enterprise AI adoption and governance.

AI Adoption Is Now an Everyone Problem: What Walmart, The New York Times, and Honeywell’s Filings Reveal

AI adoption is reshaping retail and manufacturing. See what Walmart, The New York Times, and Honeywell filings reveal.
Read post

Frequently Asked Questions

Why do organizations struggle to govern AI agents?
Most teams build agents to work first and scope them second. It's common practice, not a failure, but it leaves agents over-permissioned and spread across platforms, with no single inventory of what each one can reach. Few teams have the resources to track all of that by hand. That's why the critical first step is a one-week Identity & Exposure Audit that shows exactly what you're already running.
Does AI governance slow down innovation?
It shouldn't. The goal is balance: governance built in at the point an agent is created, rather than a review bolted on at the end. When identity and policy come standard, teams can ship new agents without starting a fresh security review every time, so innovation moves faster, not slower.
How do you keep AI agents compliant with regulations like the EU AI Act?
Every action is logged, tamper-evident, and mapped to the frameworks you answer to, including NIST AI RMF, which provides guidance for mitigating AI risks, the EU AI Act, and sector-specific requirements. As regulations change, we adapt the policy definitions and versioning to address new rules, so your agents stay compliant as the rules shift without slowing delivery.
How do you evaluate our readiness before building anything?
The Identity & Exposure Audit comes first. We inventory every agent, service account, and non-human identity, then evaluate what each one can reach against what its task actually requires. We also benchmark your ratio of machine identities to employee identities against industry data. You get a risk map and a governance strategy in a week, before anything new is designed.
Can one governance layer cover agents across AWS, GCP, and Databricks?
Yes. Each platform ships its own native controls, but they only cover their own technology. Our layer runs in parallel across all of them, with one inventory, one enforcement standard, and consistent identity verification everywhere. After we evaluate what each identity can reach, we check outputs the same way teams verify high-impact decisions, to ensure accountability and identify AI risks across lifecycles in line with NIST AI RMF guidance. That matters most for agents that touch sensitive records or act on behalf of customers.
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