IT and security leaders are often feeling pressure to deploy AI tools faster than the organization is ready for them. As a result, what starts off as AI agent integration and AI-assisted workflow leads to terabytes of sensitive information flowing through systems that employees assume are secure.

The numbers confirm what many already sense:

• 18% of organizations have identity and access management (IAM) roles with critical or high excessive permissions that AWS AI services can instantly assume.
• 63% of organizations lack AI governance policies to manage AI or prevent the proliferation of shadow AI.
• 87% of organizations have deployed AI assistants beyond the pilot stage, yet more than half describe their security posture as catching up, inconsistent, or reactive.

Effective AI adoption delivers today’s competitive advantage. The question is whether your governance infrastructure is growing at the same rate as your deployment.

As AI use proliferates, enterprises need to treat AI and data governance as a foundational layer rather than an afterthought. Failure to do so puts teams at risk of facing a costly, post-breach cleanup and exposes the business to long-lasting reputational damage.

Here are five AI best practices that security and IT leaders can act on today.

Tip 1: Determine Who Can Access AI Features in the First Place

AI tools can read from a wide variety of file and data types. Give an employee access to an AI assistant connected to your cloud tenant, and you have effectively given that AI assistant access to everything that employee can see: sensitive records, client files, and regulated data. This exposure could lead to data misuse and leakage.

Role-based access control (RBAC) is your first line of defense here. It ensures that AI features are only available to personnel who actually need them, based on job function, department, or security clearance.

In practice, this means:

• Restricting AI tool access based on employee roles and data sensitivity, particularly in environments subject to HIPAA or FINRA requirements.
• Integrating your AI access controls with your existing identity and access management (IAM) system, such as Azure AD or Okta, so permissions are centrally governed and not managed in silos.
• Running regular audits of who has privileged access to AI features, removing permissions for staff who have changed roles or moved departments.
  
  

Tip 2: Classify Your Data Before Your AI Tools Touch It

If your data is unclassified, your AI tools have no way of knowing that a document contains protected information such as health records, financial data, or personally identifiable information. AI tools may well treat that data as public information.

Here’s what you can do to implement baseline data governance for AI:

• Use automated or manual classification tools to tag data as confidential, internal use, or public.
• Enforce tagging policies at the system level so that AI tools can recognize sensitivity labels and handle content accordingly.
• Ensure that your data ingestion pipelines respect classification boundaries. AI models should never be trained or prompted with restricted data unintentionally. 
  
  

Tip 3: Treat AI Activity Logs the Same Way You Treat System Logs

Security teams have spent years building visibility into their network environments. AI tools deserve the same scrutiny. Without centralized logging, AI usage becomes a black box, an active part of your environment that leaves no auditable trace. That said, integrating AI activity logs into your SIEM gives your team the visibility they need to flag anomalies and investigate issues.

You can:

• Route logs of AI interactions, including prompt history and access attempts, into your SIEM for centralized analysis alongside other security telemetry.
• Configure alerts for unusual patterns, such as high-volume queries, bulk data access, or requests that fall outside normal working hours.
• Review AI usage logs as part of your routine security audits, not as a separate or occasional exercise. 
  
  

Tip 4: Vet Your AI Vendors as Rigorously as Any Other Third Party

Most organizations have a rigorous process for vetting third-party software. Why should it be any different for AI tools?

With new AI product features being shipped so frequently, companies need to stay vigilant about their vendor’s compliance posture.

It’s wise to:

• Assess AI vendors against your existing third-party risk framework, covering cybersecurity, data privacy, and business continuity.
• Ask specific questions in your vendor assessments: How is training data handled? Can the model be audited for explainability? What is the incident response process if data exposure occurs?
• Set a regular review cadence for both new tools and existing vendors to ensure their compliance posture keeps pace with evolving standards and your own regulatory requirements.
  
  

Tip 5: Monitor AI Regulatory Guidance as Closely as You Monitor Threats

AI regulation is not static. The EU AI Act, the NIST AI Risk Management Framework, and sector-specific guidance from bodies like HHS and FINRA are all evolving. Organizations that only respond to regulatory changes after they become enforceable will always be playing catch-up.

AI data governance requires someone to own the regulatory watch function:

• Assign a compliance lead or legal advisor specifically responsible for tracking how AI regulations apply to your organization’s use cases, not just your industry broadly.
• Monitor updates from relevant bodies on an ongoing basis, not just during annual compliance reviews.
• Adjust internal policies and staff training whenever material regulatory changes occur, treating compliance as a living program rather than a static document.
  
  

The Cost of Waiting

Speed of adoption is easy to celebrate. However, the hidden cost of rapid innovation only reveals itself when something goes wrong.

We’ve seen many companies become ‘AI-powered’ organizations with tool integration alone. This ‘move fast and break things’ mindset forgoes the gears that keep enterprises running properly, such as AI data governance, data classification, monitoring, and regulatory awareness.

All Covered’s security and compliance consulting services are built to help organizations do exactly that, with guidance specific to your industry, your tools, and your risk appetite.

For a comprehensive breakdown of all 10 security tips, including data residency requirements, DLP configuration for AI-generated content, and consent policies for AI interactions, download the full guide: 10 Tips for Maintaining Strong Cybersecurity and Compliance with AI-Enabled Cloud Tenants.

If you’d like to reach one of our experts, book a free security consultation, and we’ll help you identify where your AI integration risks lie and what to do about them.