AI Governance & Compliance Advisory
Nobody needs a permit
to change a lightbulb. Everybody needs one to open a live panel.
Responsible-AI policies, data handling, and the controls to back them, sized to what a wrong answer actually costs, mapped to the rules that apply to you, and written so your team can follow them without asking.
Oversight schedule
Support desk · v3
Where the line falls is a business decision more than a technical one. We bring the tiers. You decide what a wrong answer costs.
Mapped
To your regulations
Plain English
Policies people follow
Proportionate
Sized to the risk
Ongoing
A review cadence
The brief
The risk isn't the AI. It's AI with no guardrails.
A model that leaks customer data, makes a biased call, or acts without oversight can cost you far more than it ever saved. Governance is how you take the upside without those downsides: clear rules for what data it touches, where a person must decide, and how you'd prove any of it if somebody asked.
The trick is proportion. A plant doesn't require a permit to change a lightbulb, and it absolutely requires one to open a live panel. Same principle, applied to what your AI is allowed to do on its own.
Enough control to be safe. Not so much that nobody can ship.
What the rules are for
Adopt AI without the landmines
Data, privacy, and risk handled properly from the start instead of after an incident, which is the only time governance is ever cheap to do.
Plain English, not legalese
Policies your team can actually follow, that also satisfy the people who ask hard questions. A policy nobody reads is a liability wearing a compliance costume.
Practical and proportionate
Controls sized to your risk and your business: enough to be safe, and never so much that shipping anything requires a committee.
What we cover
Six areas, and none of them are optional in a regulated room
Data governance
What data AI can touch, how it's stored and retained, and the name of the person accountable for it.
Privacy & PII
Detection, redaction, and consent, so personal data is handled lawfully instead of hopefully.
Security
Access controls, logging, and vendor review, so your AI surface doesn't quietly become a new hole in the wall.
Fairness & bias
Checks for biased or unfair outcomes, with a plan to test for them and a route to correct them.
Human oversight
Explicit rules for where a person must review or approve before an AI decision lands on somebody.
Regulatory readiness
Mapping to the rules that actually apply to you: privacy law, sector rules, and emerging AI regulation.
The engagement
Six weeks, and then it never quite finishes
Assess
We review how AI touches data, decisions, and customers across the business as it runs today, including the things running without anyone having decided they should.
Set the policy
Clear, usable policies for data, privacy, oversight, and acceptable use, written for the people who have to follow them instead of for a shelf.
Put the controls in
Redaction, access, logging, and the review gates. The point where policy stops being a document and starts being something the system enforces.
Review and move the line
Your AI footprint grows, the rules change, and the line moves with them. Governance set once and never revisited is governance that quietly stops being true.
The method
Four moves, and what each one leaves behind
Assess
A map of where AI touches data, decisions, and customers
Set policy
Acceptable use, data handling, and oversight rules
Put controls in
Redaction, access, logging, and the review gates
Monitor
A risk register and a review cadence that survives you
Sizing the control
Four tiers, and most of your work lives in the first two
The mistake is applying the top tier to everything, which feels responsible and gets routed around by March. Each action lands in the tier its consequence earns, and nothing more.
No permit
Runs free, loggedLow-consequence actions where being wrong is cheap and reversible. Drafting, tagging, routing. The log exists so you can answer questions later, and nobody has to approve each one.
Sampled
Runs free, checked afterMedium-consequence work that would grind to a halt behind an approval queue. It runs, and a sample gets reviewed on a cadence so drift is caught without a human in every loop.
Permit
A person approves firstHigh-consequence actions where being wrong costs money or trust. The AI prepares the decision and a person makes it, which is faster than it sounds because the work is already done.
Two-person
Two people, and a recordIrreversible or regulated actions. Deletion, disclosure, anything you'd have to explain. Two sign-offs and a record, because this is the tier where you'll be asked to prove it.
What the line changes
What written rules actually buy you
The team stops freezing
Most AI hesitancy is ambiguity dressed as caution: nobody knows whether they're allowed to. A written line means people ship the low-risk things without asking permission.
The hard question has an answer
When a customer, an auditor, or a board member asks how you control this, you have a document and a log instead of a meeting and a promise.
The gate is where the risk is
Governance that gates everything gets routed around within a month. Gating only what deserves it is the version that survives contact with a deadline.
You find out before they do
Assessing what's already live is usually the highest-value hour of the engagement, because that's where the surprises are.
Selected work
Where somebody drew the line
What you're left holding
Policies, controls, and a way to keep them
Everything your team, your customers, and your board need in order to believe your AI is handled responsibly, and the log to prove it when believing isn't enough.
- 01An AI governance and acceptable-use policy
- 02A data-handling and retention framework
- 03PII detection, redaction, and access controls
- 04Human-oversight rules, with the line drawn per action
- 05A risk register with mitigations against each entry
- 06A monitoring and review cadence you can run without us
Where the line sits lowest
Every sector draws it somewhere different
Healthcare
Where the line sits low and the record matters as much as the decision, because you'll be asked to reconstruct both.
Financial services
Regulated decisions, explainability, and audit trails. The tier list here is longer and the two-person tier is busier.
Legal & professional services
Confidentiality and privilege, where the question is less what the AI decided and more what it was ever allowed to read.
Software & platforms
Usually the fastest movers with the least governance, and the ones who discover the gap when an enterprise buyer sends a questionnaire.
When it stops being optional
You're in a regulated industry
Healthcare, finance, legal: places where an AI misstep with data carries consequences that aren't measured in engineering hours.
You're scaling past the pilot
One pilot needs judgment. Ten need rules, or the tenth one will be doing something nobody signed off on.
Enterprise buyers are asking
Your customers or your board want it in writing that your AI is safe, fair, and compliant, and a confident verbal answer is no longer landing.
Start with what's live
Find out what your AI is already allowed to do.
Most teams discover the answer is more than they thought, and that nobody decided it.
Choosing Flaidex for this
What we are, and what we aren't
We are not a law firm
We don't give legal advice and we don't certify anything, and we say so up front. We build the practical controls and map them to what applies, so your legal and compliance people have something concrete to sign off instead of a blank page.
We draw the line with you
Where autonomy stops is a business decision more than a technical one. We bring the tiers and the questions; you decide what a wrong answer costs, because it's your money and your customers.
Proportionate, or it gets ignored
Controls sized past the real risk don't make you safer. They make people route around the process, which is how you end up with governance on paper and none in production.
We assess what is already live first
It's rarely too late, and it's usually where the risk is. The things running today were often never decided on. They accumulated.
Working with us
What the engagement itself is like
A few weeks, then a cadence
Roughly six weeks to assess and produce the policies and controls, then a light-touch quarterly review to keep it current as your AI footprint grows.
Written for two audiences
One set of documents that an engineer can implement and a compliance officer can sign, instead of a technical annex nobody opens and a policy nobody can act on.
Built to outlive us
The register, the cadence, and the gates are documented so your team runs them. Governance that depends on a retainer isn't governance.
Questions
What people ask about governance
Q1Do you provide legal advice or certification?
No. We aren't a law firm or a certifying body, and we're clear about that. We put practical governance and controls in place and map them to the regulations that apply to you, so your legal and compliance teams have something concrete to sign off on instead of a blank page.
Q2Which regulations do you cover?
We work to the ones relevant to you: data-protection law like GDPR, sector rules in areas like healthcare and finance, and emerging AI regulation such as the EU AI Act. We map your use cases to what applies and design controls to match.
Q3Isn't governance just red tape that slows us down?
Done badly, yes. Done well, it's the opposite: clear rules mean your team can ship AI confidently instead of freezing over whether they're allowed to. We size the controls to your actual risk so they protect you without grinding you to a halt.
Q4We already have some AI in production. Is it too late?
Not at all, and it's exactly when this matters most. We assess what's live, find the gaps, and put governance around it. That's often the highest-value place to start, before scale multiplies the risk.
Q5How do human-oversight rules work?
We define where an AI decision can act on its own and where a person must review or approve first, based on the impact if it's wrong. High-stakes actions get a human gate; low-stakes ones run freely with logging.
Q6How long does this take?
A focused governance engagement is usually a few weeks to assess and produce the policies and controls, then an ongoing light-touch review cadence to keep it current as your AI footprint grows.
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