A CISO’s guide to securing AI models

In AI applications, machine learning (ML) models are the core decision-making engines that drive predictions, recommendations, and autonomous actions. Unlike traditional IT applications, which rely on predefined rules and static algorithms, ML models are dynamic—they develop their own internal patterns and decision-making processes by analyzing training data. Their behavior can change as they learn from new data. This adaptive nature introduces unique security challenges.

Securing these models requires a new approach that not only addresses traditional IT security concerns, like data integrity and access control, but also focuses on protecting the models’ training, inference, and decision-making processes from tampering. To prevent these risks, a robust approach to model deployment and continuous monitoring known as Machine Learning Security Operations (MLSecOps) is required.

To understand how MLSecOps can help protect AI models in production environments, let’s explore the four key phases of ML model deployment.

Release

The Release phase is the final checkpoint before an AI model enters production. During this phase, the model undergoes extensive testing and security validation. Key security checks include packaging the model into a secure environment, ensuring compliance with regulatory frameworks, and signing the model digitally to guarantee its integrity.

Consider a financial services company deploying a fraud detection model. Before release, the security team ensures that the model and its dependencies are well-documented, and that the model complies with necessary regulations like GDPR or SOC 2. This process helps identify potential vulnerabilities, such as unvetted open-source libraries, that could expose the company to attacks.

Deploy

Once a model is released, it moves to the Deploy phase, where security measures are implemented to ensure the model’s safety in a live environment. A common practice is the use of policies as code, where security rules are enforced automatically during deployment.

For example, an e-commerce platform using machine learning for inventory prediction might set up automated policies to monitor the security posture of their models. If a model exhibits risky behavior—such as unauthorized access or unusual data manipulation—those policies can trigger an automatic rollback or removal from production. This approach helps to ensure that only secure models are deployed and that any potential risks are mitigated in real-time.

Operate

After deployment, AI models must be continuously secured as they operate in live environments. This is where runtime security measures like access controls, segmentation and monitoring for misuse become essential.

For example, segmentation policies can be applied to restrict access to the model, ensuring that only authorized personnel can interact with it. In addition, organizations should monitor user behavior for signs of misuse or potential threats. If suspicious activity, such as an unusual pattern of API requests, is detected, security teams can adjust access controls and take appropriate measures to protect the model from further exploitation.

Monitor

AI models are not static, and over time, their performance may degrade due to model drift or decay. Monitoring for these issues is crucial to ensuring that the model continues to perform as expected in production environments.

Monitoring also plays a critical role in detecting security threats. For example, adversarial attacks—where malicious actors manipulate inputs to force the model into making incorrect predictions—can be caught through continuous anomaly detection and performance monitoring. Without this level of vigilance, these attacks could cause long-term damage before being detected.

Best practices

To safeguard ML models from emerging threats, CISOs should implement a comprehensive and proactive approach that integrates security from their release to ongoing operation. The following best practices provide a framework for building a robust defense, ensuring that ML models remain secure, compliant, and resilient in production environments:

1. Automate security in the Release phase: Ensure that models undergo automated security checks, including compliance validation and digital signing, before deployment.
2. Implement real-time policy enforcement: Use policies as code to automatically enforce security rules during the deployment phase, preventing insecure models from going live.
3. Continuously monitor for drift and threats: Ongoing monitoring for model drift, decay, and misuse is critical to maintaining the performance and security of AI systems in production.
4. Deploy multi-layered security: Implement controls such as rate limiting to manage the number of requests and improve availability, segmentation, and real-time threat detection to protect models during operation.

Implementing security measures at each stage of the ML lifecycle—from development to deployment—requires a comprehensive strategy.

MLSecOps makes it possible to integrate security directly into AI/ML pipelines for continuous monitoring, proactive threat detection, and resilient deployment practices. As ML Models become increasingly embedded in enterprise workflows, adopting MLSecOps is critical for protecting these powerful decision-making engines.