Artificial intelligence is rapidly being integrated into enterprise systems, reshaping business operations and decision-making processes. But as organizations rush to leverage AI, they also introduce new risks and vulnerabilities. Cybersecurity professionals face mounting pressure to protect these systems from threats like data poisoning, prompt injection attacks, and sensitive data exposure—without compromising their functionality.

While many assume that securing AI is no different from protecting other applications, the reality is that AI introduces a new set of challenges that demand a fresh approach.

Recent incidents illustrate this unsettling dynamic. In the Copilot breach, an employee asked the system to summarize company emails, only to receive confidential executive messages, HR records, and private files. Similarly, DeepSeek's chat history exposure revealed that users’ interactions were stored and accessible to others.

These breaches highlight a growing problem: AI is evolving faster than our ability to secure it.

The evolving attack surface

For AI to deliver real business value, it requires broad access to enterprise data, systems, and applications. However, this openness increases the risk of unauthorized data exposure, unreliable outputs, and data manipulation. Unlike traditional software, AI operates dynamically and continuously learns from new inputs, making risk management an ongoing challenge.

This dynamic behavior creates a constantly evolving attack surface. AI systems are vulnerable to prompt injection attacks, where malicious instructions are embedded in user inputs, causing the model to reveal sensitive data or perform unauthorized actions. Over-reliance on AI automation can lead to operational issues if errors in model predictions go unchecked, especially when AI is integrated into critical processes such as financial transactions or compliance operations.

Data poisoning and adversarial inputs can also corrupt outputs, leading to unreliable or biased decisions. AI agents present a new challenge—often requiring access to sensitive data to function. Without context-based access controls, these agents may be exposed to unauthorized information or make decisions based on incomplete or inappropriate data.

These risks create significant compliance, reputational, and financial challenges.

Establishing a control framework for AI security

Securing AI in enterprise environments requires a comprehensive control framework that covers the entire lifecycle of AI systems, from data collection and model training to deployment and continuous monitoring. The foundation of this framework is data integrity. Organizations must ensure that the enterprise data used by AI is accurate, traceable, and appropriately secured. Establishing a clear chain of custody for data, tracking its origins and transformations, and monitoring its usage throughout the AI lifecycle is essential.

Active data governance practices, including data lineage tracking and anomaly detection, are crucial for maintaining control. These measures not only meet compliance requirements but also provide granular visibility into AI decision-making. By identifying unusual patterns in training data, security teams can detect potential tampering or malicious activity early.

To minimize risks, strict access boundaries must be set for AI agents and retrieval systems before deployment. Using high-quality, verified data helps reduce biases and manipulation risks, while testing AI models in controlled environments helps identify adversarial threats. Full visibility into training and retrieval data is also necessary to prevent data poisoning and unauthorized access.

Organizations should implement real-time monitoring and anomaly detection systems to identify and mitigate threats before they escalate. Automated security alerts and response mechanisms can neutralize risks before AI-driven decisions cause harm. However, human oversight remains essential for maintaining accountability and addressing complex security scenarios. Regular audits of AI decision logs help refine governance policies and ensure compliance.

We can’t get ahead, but we can keep up

As AI continues to evolve, so will the associated security risks. Cybersecurity professionals must remain vigilant and proactive, continuously monitoring emerging threats and adapting security strategies accordingly. By implementing robust control frameworks that emphasize data integrity, secure model development, granular access control, and continuous monitoring, organizations can effectively mitigate risks while building trust and accountability.

^{This blog was first published on Security Boulevard, on march 3, 2025.}