AI changes how your organization uses and exposes data, so traditional security controls on their own are no longer enough.

AI applications:

• Thrive on data – They often connect to many internal systems and data sources to be useful, which increases the risk of data leakage and oversharing.
• Integrate deeply into workflows – AI can make real-time, high-stakes decisions, from security alerts to customer responses, so errors or manipulation can have immediate impact.
• Are already in use, even if unapproved – Employees may adopt consumer-grade tools (shadow AI) without IT approval, which can bypass your normal controls.

Microsoft’s research shows that 47% of current users of AI for security are very confident in its ability to make critical security decisions. That confidence comes when AI is deployed with the right safeguards.

A dedicated AI security strategy helps you:

• Address top risks: data leakage, emerging threats (like prompt injection), and compliance challenges.
• Align with evolving regulations such as the EU AI Act, GDPR, HIPAA, and DORA, which expect transparency, accountability, and robust governance for AI.
• Apply a Zero Trust approach to AI: never trust, always verify every interaction, identity, and data access.

In practice, this means putting in place AI-specific governance, security controls, and lifecycle management so you can use AI confidently without exposing sensitive information or falling out of compliance.

You can manage AI risk by focusing on three areas: data protection, threat mitigation, and error handling.

1. Protect against data leakage and oversharing

• Control shadow AI: Set clear policies that employees must use only vetted AI tools. Monitor for unapproved usage and offer secure, enterprise-grade alternatives so teams don’t feel the need to go around IT.
• Right-size permissions: Avoid over-permissioned data. Use role-based access controls (RBAC) so AI systems only see the data needed for a given role. Monitor AI usage like you would search activity to spot inappropriate access.
• Manage the data lifecycle: Automate retention and deletion policies. Poor retention (for example, keeping customer purchase history beyond regulatory limits) increases exposure when AI tools access old or unnecessary data.

2. Address emerging threats like prompt injection

• Validate and sanitize inputs: Treat all user-provided or external content as untrusted. Clean and validate it before the AI processes it.
• Limit access to sensitive data: Don’t give AI models broad access by default. Restrict what they can read or act on, especially in high-risk scenarios.
• Strengthen identity checks: Require strong authentication and authorization for users interacting with sensitive AI workflows.

3. Manage AI errors and quality issues

AI can make mistakes such as:

• Hallucinations – adding unsupported information.
• Omissions – leaving out critical details.
• Bias – skewed outcomes by race, gender, or other factors.
• Garbage in, garbage out (GIGO) – poor or malicious input leading to flawed results.
• Skewed focus – optimizing for the wrong priorities.
• Overreliance – users trusting AI outputs without review.

To reduce these risks:

• Implement monitoring and validation to review AI outputs, especially in customer-facing or high-impact use cases.
• Use commercial AI tools with built-in safeguards such as bias detection, input sanitization, and access controls.
• Keep humans in the loop for complex or high-risk decisions, and define clear escalation paths when AI is uncertain.

By combining policy, technical controls, and human oversight, you can adopt AI at pace while keeping data exposure, attacks, and errors within acceptable risk levels.

A practical way to structure AI adoption is to use a phased framework grounded in Zero Trust principles, with three core phases: Govern AI, Secure AI, and Manage AI.

Phase 1: Govern AI

• Define policies for responsible AI use across the organization.
• Assess risk levels of AI workloads and classify them appropriately (for example, higher scrutiny for healthcare diagnostics than for internal content drafting).
• Align governance with regulations such as the EU AI Act, GDPR, and DORA, including requirements for transparency, documentation, and operational resilience.
• Automate policy enforcement where possible to reduce human error.

Phase 2: Secure AI

• Apply Zero Trust: never trust, always verify identities, devices, and data access for AI systems.
• Protect data, models, and infrastructure with robust security controls and continuous monitoring.
• Address emerging threats such as prompt injection and indirect attacks, and regularly test for vulnerabilities.
• Use AI-driven security and compliance tools to monitor for issues like data drift, unauthorized access, or unexplainable outputs.

Phase 3: Manage AI

• Maintain AI models over time, monitoring performance, reliability, and fairness.
• Standardize processes for updates, retraining, and decommissioning AI systems.
• Keep documentation current on data usage, model validation, and monitoring activities to support audits and regulatory reviews.

Across all phases, focus on:

• People – Train employees on AI risks and secure usage, and guide them toward IT- and security-approved tools.
• Collaboration – Break down silos between IT, security, and business teams so AI strategy, risk, and operations stay aligned.
• Transparency – Communicate your AI security and governance approach to stakeholders to build trust.

This phased approach helps you reimagine how AI is used in the business while keeping security, compliance, and accountability at the center of your program.