Turn AI policy into decisions people can make safely.
Give your people clear rules they can use. We design practical AI policies, approval routes and controls so useful work can move forward without leaving important decisions unowned.
For teams managing AI use and operational risk across connected systems.
We map the tools and workflows that affect your business, including informal use. You can see where useful work is happening and where unclear ownership or information handling needs attention.
We turn agreed policy into practical examples and approval routes. Staff can see what is allowed, what needs a review and who can decide on an exception.
Your team needs to know what it can do with AI without sending every question to the leadership team. We turn policy into practical rules for tools, information, approvals and exceptions, then help people apply them to real work.
That means a clear route for useful ideas to move forward and a clear stop when the risk needs review. We can cover employee tools, connected business workflows or both.
Legal advice and external assurance remain specialist responsibilities; our work makes the agreed rules usable in everyday decisions rather than leaving them in a document.
Paloren S4 MethodWe map how AI tools are actually used, who owns each decision and what evidence a control must produce, so governance becomes workable rather than a policy nobody follows.
| Stage | What it changes |
|---|---|
| Signal | Build the tool inventory: which applications staff use, what data they handle, which actions touch customer records, and who currently owns each outcome. |
| Synthesis | Draft the one-page policy, approved-tools list, data boundary table and exception route, referencing ISO 42001, the NIST framework and EU AI Act classifications. |
| System | Test the controls: scoped access, approval before a sensitive action, activity records and a stop procedure, defining the evidence each check should produce. |
| Scale | Give responsible owners a review cadence for new tool requests, track exception volume and reporting-channel use, and extend governance one workflow at a time. |
A 200-person services firm formalises AI use. Signal logs 14 tools, including two that alter customer records. Synthesis produces a one-page policy, an approved-tools list and a data boundary table naming who can accept risk.
System tests a connected workflow by requiring approval before an action and checking that activity records exist. Scale tracks exception requests, aiming to cut unapproved tool use from eight reports a month to two while review evidence stays complete.
Written teaching example with hypothetical inputs, not a client result.
Operating metric: Unapproved tool use and review-evidence completeness per quarter.
What this can change for your team
How we make this work
Governance begins with an inventory of the tools and use cases in scope. Paloren identifies who uses them, what information they handle, which actions they support and who owns the outcome.
The inventory includes informal practices that affect the business, while discovery keeps a clear purpose and appropriate handling. We distinguish an employee drafting internal text from a system changing customer records because different consequences require different controls.
We reference the frameworks that apply: ISO 42001 for AI management systems, the NIST AI Risk Management Framework for governance scope, and the EU AI Act for high-risk classifications where applicable.
The result is a usable picture of responsibility and exposure mapped to your regulatory context.
How we make this work
A governance policy needs examples that help staff decide what to do under ordinary pressure. We work with responsible owners to define permitted uses, restricted data, required review and the route for requesting an exception.
A practical decision guide can distinguish an approved internal draft from an unapproved customer commitment. It also names who can approve a new tool or accept a risk. You receive a one-page policy, an approved-tools list, an approval route for new applications and a data boundary table.
Employees should not need to infer rules from a vendor's marketing language. Legal and regulatory interpretation remains with qualified advisers and the organisation's accountable teams.
We connect policy to access, approvals and checks in the actual workflow. You have evidence of what happens before an action, not just a statement that someone should review it.
How we make this work
Governance implementation asks how each important rule is enforced or checked. For a connected workflow, that may involve scoped access, approval before a sensitive action, an activity record and a stop procedure.
For employee use, it may mean an approved application list, a source-checking requirement and a reporting channel. The exact control depends on the environment and needs testing.
A policy saying that people must review output does not show that review happens before an action. We define the evidence a control should produce and who examines it. Technical configuration work is explicitly scoped, with dependencies on the administrators who own the relevant systems.
New tools, data and responsibilities should not leave the rules behind. We help you establish proportionate reviews and a clear response when something goes wrong.
How we make this work
AI governance needs a way to change when tools, processes or responsibilities change. We define review triggers such as a new data source, expanded action authority or a material incident.
An incident exercise walks through who stops the workflow, preserves relevant evidence, assesses the impact and decides whether it can resume. Routine reviews examine exceptions and control failures without treating a clean dashboard as proof of safety.
The handover includes owners, records and a manageable review cadence.
Tell Paloren where the rules are unclear or slowing work down. Reply from the team within one business day. No deck, no technical brief needed.
Talk to PalorenRecord the tools, data, actions and owners within the agreed scope.
Define permitted use, reviews, exceptions and specialist responsibilities.
Check whether important rules produce observable approval or review evidence.
Agree incident handling, change triggers and review responsibilities.
Scoped AI inventory
Acceptable-use decision guide
Responsibility and approval matrix
Control evidence register
Incident exercise findings
Review and maintenance plan
Signal → Synthesis → System → Scale
Find where intelligence creates value.
Understand the business to identify and prioritize the opportunities with the greatest measurable impact.
Translate complexity into a clear design.
Bring together the people, workflows, data, and technology to define how intelligence should work.
Turn the design into a working capability.
Build the solution that embeds intelligence into how work and decisions happen.
Compound what works.
Measure impact, optimize performance, maintain reliability, and unlock greater leverage.
From signal to scale.
No. Governance work organises requirements and decisions for review by your legal or compliance team. This programme runs 3 to 4 weeks from USD 10k. Qualified advisers should confirm obligations depend on your use case and jurisdictions before the organisation relies on a compliance conclusion.
Learners need clear rules for exercises and approved access; a full governance programme may not be necessary before an AI literacy session from USD 3,500 per cohort. For real operational use, unresolved data, approval or accountability questions should be addressed rather than left for employees to interpret during training.
Yes. Technical controls can be scoped alongside governance design over 3 to 4 weeks, with administrators and system owners involved. The proposal distinguishes documentation, configuration, testing and ongoing review.
A written recommendation is not the same as a control installed and verified in your environment.
No. This service produces the governance deliverables within 3 to 4 weeks. If your business requires independent assurance or certification, identify that requirement early so the necessary external review and evidence can be planned separately as a distinct engagement.
Could a manager decide today whether that AI request is allowed?
Talk to Paloren
