Your teams already use generative AI. The governance is what's missing.
Large language models are in your workflows whether or not you approved them. The question is no longer whether to allow generative AI, but how to govern it, usage policy, data handling, accuracy safeguards, and disclosure, before the exposure becomes an incident.
Adoption ran ahead of policy.
Generative AI spread through organizations bottom-up, faster than any technology before it. Most companies now have widespread genAI use and no coherent policy governing it.
The gap shows up as shadow usage, sensitive data pasted into public tools, unverified AI output shipped to clients, and no disclosure standard. None of it is malicious, it is simply ungoverned.
The exposure hiding in everyday use.
Three places genAI risk accumulates quietly until something forces attention.
the most common exposure is sensitive data entering tools with no retention or training controls.
Stratenity POVhallucinations reach clients when there is no verification step between AI output and delivery.
Stratenity POVpolicy areas our responsible-use standard covers, from data handling to disclosure and escalation.
Stratenity AI policyYou feel this if any of these are true.
LLM governance is for leaders whose teams use generative AI daily and who need a real policy, not a one-line ban that everyone ignores.
- Staff use public AI tools with no rules on what data may go in.
- AI-assisted work reaches clients with no verification or disclosure standard.
- You have no approved-tools list and no guidance on acceptable use.
- Legal or security has raised genAI concerns you cannot yet answer.
- Your one AI page is a legal policy, not something that guides day-to-day use.
What a generative-AI governance program puts in place.
Practical controls that let teams use genAI safely, not rules that push it underground.
Acceptable-use standard
Clear rules on approved tools, acceptable tasks, and what is off-limits, written to be followed.
Data handling & privacy
What data may enter which tools, with retention, training-opt-out, and privacy-law alignment.
Verification & disclosure
A verification step before AI output ships, and a disclosure standard for AI-assisted work.
Approved tooling
A vetted list of tools and configurations, so teams have a safe default instead of the open web.
Prohibited & high-risk uses
Named prohibited uses and an escalation path for anything sensitive or high-stakes.
Standards mapping
Aligned to the EU AI Act, NIST AI RMF, and ISO 42001 so the program is defensible.
We wrote the policy: and the practice behind it.
Stratenity runs its own consulting on governed AI, so our genAI governance is operational, not theoretical.
Our responsible-use standard for AI engines and assistants already covers model limits, accuracy, ethics, data handling, disclosure, and escalation. We adapt that to your context and stand up the controls, senior-led, across all 50 U.S. states.
From shadow usage to a governed default.
Start with a fast read on current exposure, then put the policy and controls in place.
Strategy Scan
A fast, senior-led read on your highest-stakes decision, framing, evidence, risk, and a 90-day plan. Days, not weeks.
Explore ›Advisory & Execution
Ongoing senior support that turns the decision into owners, KPIs, and an operating cadence.
Explore ›AI Governance program
Stand up the council and controls mapped to the EU AI Act, NIST AI RMF, and ISO 42001, governance you can evidence.
Explore ›Generative AI governance, delivered across all 50 U.S. states.
Delivered virtually, so senior governance support reaches your team wherever it works, from Alabama to Wyoming.
What leaders ask first.
What is LLM governance? +
We already have an AI policy. Is that enough? +
Do you work with organizations in my state? +
How is Stratenity different from a traditional top consulting firm? +
Let teams use generative AI, safely.
Framework references: EU AI Act; NIST AI Risk Management Framework; ISO/IEC 42001. Reflects Stratenity's responsible-use standard.