Microsoft guide for securing the AI-powered enterprise

Getting started with AI applications

Artificial intelligence (AI) is transforming business operations, unlocking innovation while introducing new risks. From shadow AI (consumer-grade tools adopted without oversight) to prompt injection attacks—and evolving regulations like the EU AI Act—organizations must address these challenges to use AI securely.

This guide covers the risks associated with AI: data leakage, emerging threats, and compliance challenges along with the unique risks of agentic AI. It also provides guidance and practical steps to take based on the AI Adoption Framework. For deeper insights and actional steps download the guide. AI is a game changer—but only if you can secure it. Let’s get started.

80% of leaders cite data leakage as a top concern. ¹Shadow AI tools—used without IT approval—can expose sensitive information, increasing breach risks.
88% of organizations worry about bad actors manipulating AI systems.²Attacks like prompt injection exploit vulnerabilities in AI systems, highlighting the need for proactive defenses.
52% of leaders admit uncertainty about navigating AI regulations.³Staying compliant with frameworks like the EU AI Act is essential for fostering trust and maintaining innovation momentum.

Agentic AI: Key risks and how to address them

Agentic AI offers transformative potential, but its autonomy introduces unique security challenges that require proactive risk management. Below are key risks and strategies tailored to address them:

Hallucinations and unintended outputs

Agentic AI systems can produce inaccurate, outdated, or misaligned outputs, leading to operational disruptions or poor decision-making.

To mitigate these risks, organizations should implement rigorous monitoring processes to review AI-generated outputs for accuracy and relevance. Regularly updating training data ensures alignment with current information, while escalation paths for complex cases enable human intervention when needed. Human oversight remains essential to maintain reliability and trust in AI-driven operations.

Overreliance on AI decisions

Blind trust in agentic AI systems can lead to vulnerabilities when users act on flawed outputs without validation.

Organizations should establish policies requiring human review for high-stakes decisions influenced by AI. Training employees on AI limitations fosters informed skepticism, reducing the likelihood of errors. Combining AI insights with human judgment through layered decision-making processes strengthens overall resilience and prevents overdependence.

New attack vectors

The autonomy and adaptability of agentic AI creates opportunities for attackers to exploit vulnerabilities, introducing both operational and systemic risks.

Operational risks include manipulation of AI systems to perform harmful actions, such as unauthorized tasks or phishing attempts. Organizations can mitigate these risks by implementing robust security measures, including real-time anomaly detection, encryption, and strict access controls.

Systemic risks arise when compromised agents disrupt interconnected systems, causing cascading failures. Fail-safe mechanisms, redundancy protocols, and regular audits—aligned with cybersecurity frameworks like NIST—help minimize these threats and bolster defenses against adversarial attacks.

Accountability and liability

Agentic AI often operates without direct human oversight, raising complex questions about accountability and liability for errors or failures.

Organizations should define clear accountability frameworks that specify roles and responsibilities for AI-related outcomes. Transparent documentation of AI decision-making processes supports error identification and liability assignment. Collaboration with legal teams ensures compliance with regulations, while adopting ethical standards for AI governance builds trust and reduces reputational risks.

Get started with a phased approach

With new AI innovations like agents, organizations must establish a strong foundation based on Zero Trust principles— “never trust, always verify.” This approach helps ensure that every interaction is authenticated, authorized, and continuously monitored. While achieving Zero Trust takes time, adopting a phased strategy allows for steady progress and builds confidence in securely integrating AI.

Microsoft’s AI adoption framework focuses on three key phases: Govern AI, Manage AI, and Secure AI.

By addressing these areas, organizations can lay the groundwork for responsible AI use while mitigating critical risks.

To succeed, prioritize people by training employees to recognize AI risks and use approved tools securely. Foster collaboration between IT, security, and business teams to ensure a unified approach. Promote transparency by openly communicating your AI security initiatives to build trust and demonstrate leadership.

With the right strategy, grounded in Zero Trust principles, you can mitigate risks, unlock innovation, and confidently navigate the evolving AI landscape.

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