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

AI applications:

• Thrive on data – They often connect to multiple internal systems and data sources, increasing the risk of data leakage and oversharing. For example, an unvetted chatbot used by marketing could accidentally pull in sensitive customer or financial data.
• Integrate deeply into workflows – AI can make real-time, high-impact decisions, from customer responses to risk assessments. According to Microsoft Security research, 47% of current users of AI for security say they are very confident in AI’s ability to make critical security decisions. That confidence only makes sense if the AI is properly governed and secured.
• Introduce new attack surfaces – Threats like prompt injection attacks and manipulation of AI outputs are emerging and are not fully addressed by legacy security models.
• Operate in a shifting regulatory landscape – Regulations such as the EU AI Act, DORA, GDPR, and HIPAA are raising expectations for transparency, documentation, and governance of AI systems.

Because of this, organizations are reimagining their security approach around AI, focusing on:

• Govern AI – Define policies, risk assessments, and responsible use guidelines.
• Secure AI – Protect data, models, and infrastructure using a Zero Trust mindset: never trust, always verify.
• Manage AI – Continuously monitor performance, risks, and compliance over the full lifecycle.

This dedicated strategy helps you capture AI’s business value while keeping data, systems, and people protected.

The guide highlights three main risk areas you should focus on first: data leakage, emerging threats, and compliance challenges.

1. Data leakage and oversharing

• Shadow AI: Employees quietly adopt consumer-grade AI tools to be more productive, but these tools may not be approved or monitored. This can expose sensitive customer or internal data.
• Over-permissioned data: AI tools often inherit a user’s full access. If a marketing analyst can see financial records, the AI may surface that data in responses, even when it’s not needed.
• Weak data lifecycle management: Retaining data longer than necessary (for example, old customer purchase histories) increases the chance that AI will access or process information that should have been deleted.

How to respond: Centralize policies for approved AI tools, enforce role-based access controls (RBAC), monitor AI usage like you monitor search activity, and automate retention and secure deletion.

2. Emerging threats and AI-specific vulnerabilities

• Prompt injection attacks: Malicious instructions hidden in content (documents, websites, emails) can trick AI systems into revealing confidential data or performing unintended actions.
• AI errors: Issues such as hallucinations, omissions, bias, poor-quality input (GIGO), skewed focus, and overreliance on AI outputs can all lead to bad decisions or unfair outcomes.

How to respond: Validate and sanitize user inputs, limit model access to sensitive data, require identity verification for high-risk interactions, and implement monitoring and validation to catch errors before they impact customers or operations.

3. Compliance and regulatory pressure

• Many leaders admit they are unsure how to navigate changing AI regulations, including the EU AI Act, DORA, GDPR, and HIPAA.
• Without clear governance and documentation, it’s hard to prove how AI systems use data, make decisions, and stay within regulatory boundaries.

How to respond: Build governance frameworks aligned to regulations, maintain detailed documentation (data usage, model validation, monitoring), and use AI-driven compliance tools to continuously check for issues like data drift, unexplainable outputs, or unauthorized access.

By tackling these three areas first, you create a practical foundation for secure, compliant AI adoption across the business.

The guide recommends a phased approach grounded in Zero Trust principles and an AI adoption lifecycle with three core phases: Govern AI, Secure AI, and Manage AI.

Phase 1: Govern AI

Begin by setting the rules of the road:

• Define policies for responsible AI use across the organization.
• Assess risk levels for different AI workloads (for example, internal productivity tools vs. customer-facing decision systems).
• Align with ethical standards and regulations such as the EU AI Act and DORA.
• Automate policy enforcement where possible to reduce human error.

Phase 2: Secure AI

Once governance is in place, focus on protection:

• Apply Zero Trust: never trust, always verify every user, device, and AI interaction.
• Protect data, models, and infrastructure with strong access controls, monitoring, and threat detection.
• Address AI-specific threats such as prompt injection and over-permissioned access.
• Run regular risk assessments to keep pace with new attack methods and business use cases.

Phase 3: Manage AI

Finally, treat AI as a living system, not a one-time deployment:

• Monitor model performance and behavior over time to catch data drift or degradation.
• Maintain clear documentation of deployments, updates, and monitoring activities.
• Set up standardized processes for updates, retraining, and decommissioning AI systems.

Across all phases, the guide emphasizes three enablers:

• People: Train employees on AI risks and safe usage, and encourage use of IT- and security-approved tools instead of shadow AI.
• Collaboration: Break down silos between IT, security, and business teams so AI security decisions support real business goals.
• Transparency: Communicate AI security initiatives openly to build trust with customers, employees, and regulators.

This structured approach helps you adopt AI with confidence, balancing innovation with security and compliance from day one.