Generative Al adoption isn’t slowing down. Across enterprises, business teams are already using tools like ChatGPT, Copilot, and other Al-powered assistants-often without formal approval.
These tools boost productivity but also pose risks.
Unvetted Al usage can lead to data leakage, regulatory exposure, and reputational damage. According to a recent report on GenAl data leakage, 8.5% of employee prompts included sensitive data posing a serious threat to a company’s security, privacy, legal and regulatory compliance. And most organisations don’t know it’s happening until it’s too late.
For digital governance leaders, the question isn’t whether Al should be used-it’s how to define enforceable policies aligned with business goals, legal obligations, and data standards.
This article outlines a practical, phased framework to build a responsible Al governance model.
Business users are using with generative tools in ways leadership often doesn’t see-until risks surface:
These are driven by productivity, but lead to regulatory scrutiny, data leaks, and confusion.
The issue isn’t the tools-it’s the lack of oversight. Few enterprises have a policy that defines:
Most cybersecurity strategies still focus on systems-not user behaviour. But Al adoption starts at someone’s keyboard.
Policies exist but often are not designed for how Al is used. Many respond with vague guidelines or blanket bans, which do not reduce risk but just delay exposure. Here are the most common failures we see in GCC organisations, and what they look like in practice:
Teams often start using Al tools before confirming if they’re safe, compliant, or even permitted. There’s usually no vetted list of approved platforms, and no formal risk review process for free-to-use plugins or SaaS-based tools. Without a policy-first approach, governance becomes reactive and fragmented.
No one is tracking what types of data employees are feeding into Al tools. Sensitive numbers, customer records, even internal strategy documents-all of it can be shared with third-party models without controls or logs. If you oversee compliance or data protection, this is a quiet breach waiting to surface. And in regulated sectors, it may already count as one. What you don’t see in usage patterns can still show up in audit findings.
When Al-generated content introduces risks like misleading claims, biased responses, and hallucinated figures, no one knows who is accountable for the mistake. This ambiguity is a governance risk that shows up during crises.
Al spans IT, Legal, HR, and Risk-but they often work in silos. This leads to conflicting rules and poor enforcement. Without shared ownership, policy coordination breaks down. If you’re building a cybersecurity strategy that spans the enterprise, this fragmentation will derail it. A policy that isn’t jointly owned won’t hold. And gaps in coordination are where policy failure begins.
Creating an Al policy is not a legal formality but an operational directive that defines how business users innovate, protect data, and stay within regulatory bounds. We recommend a five-step governance model that balances productivity and control across departments.
Don’t start with tools. Start with business needs and associated risks. Jumping straight to tool approval can lead to inconsistent policy enforcement. Worse, it blinds you to how Al is being used at the decision layer, where the real risk lives. Instead, start by identifying legitimate, value-generating use cases.
For example:
By scoping use cases first, you gain two advantages:
This also gives business leaders ownership in shaping the policy, instead of treating it as an IT gatekeeping function.
Map high-frequency tasks across
departments (e.g..HR, Marketing.
Finance, Operations)
Identify where Al could reduce effort without introducing business or complie
Flag high-risk use cases that should be disallowed or deferred
Document these use cases as the foundation for your policy language
Pro tip: This exercise often reveals shadow usage you didn’t know existed. Use it to inform your data protection, Al governance, and broader cybersecurity strategy planning.
Good policies are easy to understand and act on. If the language is too abstract, too legal, or too technical, it will be misunderstood or ignored.
Many policies also fail because they focus on permissions instead of process. For instance, stating that “financial data must not be shared with public Al tools” is useful only if employees know how to classify data, or who to ask for approval.
A weak policy language would say, “Avoid using sensitive data in Al platforms. A stronger, operational version will say, “Financial, personal.
A weak policy language would say, “Avoid using sensitive data in Al platforms. A stronger, operational version will say, “Financial, personal. and legal data are classified as sensitive. These must not be entered into public GenAl tools like ChatGPT. For business use cases, submit a tool request via IT.”
Here are the things your Al policy should clearly state:
Pro tip: If your policy doesn’t read like something a department lead could explain to their team, it needs work.
No single team owns Al usage. That's why most Al policy failures are not technical they're organisational.
Approve or deny new Al tools based on risk, data residency, and model behaviour.
Evaluate business requests for GenAl in context-e.g.. automating candidate screening. customer responses, or report generation,
When something goes wrong. this is the team that investigates, assesses the impact, and triggers the response.
Pro tip: Keep the committee lean. Five to seven decision-makers in an organisation are optimal. The point is alignment, not bureaucracy.
Most Al risks do not come from infrastructure. They come from human decisions-made quickly, with incomplete context. Teams need to know what is permitted, what is not, and how their actions tie back to company policy, even if they are using Al to summarise meeting notes or write job descriptions.
To deliver business awareness as an organisation, the best practices include
Rolling out an Al policy across an enterprise without first testing it is a guaranteed way to invite resistance, or worse, failure.
Instead, start with a limited pilot. Choose 1-2 departments with distinct Al use cases (e.g., HR automation, marketing content development) and validate the entire governance flow: approval, usage, review, and escalation.
Use this pilot to answer the following questions:
Once the pilot holds its weight under live conditions, use the feedback to refine your policy, tool governance model, and communication cadence.
Just like any cybersecurity strategy, Al governance must be treated as a lifecycle, not a one-time deployment.
Paramount enables enterprises move from reactive controls to operational Al readiness. Our governance-first approach supports digital leaders in defining use cases, creating enforceable policies, activating cross-functional committees, and aligning Al usage with existing cybersecurity and compliance frameworks. Beyond templates, we provide hands-on support to operationalise responsible Al across teams, tools, and processes.
