5 Steps to Securing AI Workloads

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In the past year alone, the number of artificial intelligence (AI) packages running in workloads grew by almost 500%. Which is to say: AI is everywhere, and it’s settling in for the long haul. Naturally, as helpful as they are, these AI workloads come with security challenges, including data exposure, adversarial attacks, and model manipulation. So as AI adoption accelerates, security leaders must build an AI workload security program to protect their organizations while enabling innovation.

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A robust AI workload security program requires a proactive, structured approach. Here are five essential steps to ensure security and resilience in AI environments.

Step 1: Gain Visibility Into AI Workloads

Visibility is the foundation of any security program. Many organizations lack insight into where AI workloads are running, who has access, and what data they process.

To gain visibility, organizations must inventory AI workloads across cloud, on-premises, and hybrid environments. Identifying AI dependencies, such as machine learning frameworks, APIs, and data sources, is critical to understanding potential security gaps. Additionally, monitoring AI workloads in real time allows security teams to detect unusual activity, unauthorized access, and exposure risks before they become major threats.

Best Practices:

Step 2: Secure AI Development and Deployment Pipelines

AI models undergo multiple stages of development, from training to deployment. Each stage presents security risks, such as data poisoning, model theft, and insecure configurations.

Implementing DevSecOps practices ensures that security is embedded into AI model development from the start. Organizations should scan AI code and dependencies for vulnerabilities before deployment to minimize risks. Enforcing strict access controls for model repositories and training datasets is also essential to prevent unauthorized modifications and data leaks.

Best Practices:

  • Integrate vulnerability scanning for AI libraries (e.g., TensorFlow, PyTorch) into CI/CD pipelines.
  • Use infrastructure-as-code (IaC) security tools to prevent misconfigurations.

Step 3: Protect AI Workloads at Runtime

AI models are susceptible to attacks post-deployment, including adversarial inputs, model evasion, and unauthorized modifications. Runtime security is the practice of continuously monitoring and protecting workloads while they are actively running and responding to malicious activities before they cause harm. It is essential to detect and mitigate threats in real time.

Organizations must enable real-time threat detection for AI workloads using behavioral analytics to identify anomalies and malicious activities. Monitoring API interactions helps detect unusual or unauthorized requests that could compromise AI models. Security teams can also take preventative measures before malicious behavior occurs, such as enforcing least privilege access to AI models to reduce the attack surface and minimize the risk of data exposure or model manipulation.

Best Practices:

  • Leverage cloud detection and response (CDR) solutions for continuous monitoring.
  • Use anomaly detection to identify adversarial attacks against AI models.
  • Restrict access to AI APIs based on user roles and permissions.

Step 4: Manage AI Risks and Compliance

Regulatory bodies worldwide are introducing AI governance frameworks to address security, privacy, and ethical concerns. Organizations must align their AI security programs with compliance standards.

Adopting an AI risk management framework based on MITRE ATLAS and OWASP AI guidelines ensures a structured approach to AI security. Organizations should document AI security risks in a risk register and prioritize mitigation efforts. Ensuring compliance with AI regulations, such as the EU AI Act and NIST AI Risk Management Framework, is crucial for meeting legal and industry standards.

Best Practices:

  • Conduct regular AI security assessments to identify policy gaps.
  • Implement tools to audit AI model decisions.
  • Encrypt sensitive AI training data and enforce data protection policies.

Step 5: Train and Educate Security Teams on AI Threats

AI security is a rapidly evolving field, requiring security professionals to stay informed about emerging threats and defense strategies.

Developing AI security training programs for security teams and developers ensures that personnel are well-equipped to handle AI-specific threats. Conducting AI-specific threat modeling and educating teams on threats and best practices helps them anticipate potential attack vectors. Establishing an AI security incident response plan ensures a structured approach to handling AI-related breaches and minimizing damage.

Best Practices:

  • Create an AI security playbook for responding to adversarial attacks.
  • Engage in industry forums to stay updated on AI security trends.

Conclusion

Securing AI workloads is not a one-time effort but an ongoing process. By following these five steps — gaining visibility, securing pipelines, protecting runtime environments, managing risks, and educating teams — organizations can build a resilient AI security program that enables innovation while mitigating risks.

As AI adoption grows, security leaders must take proactive measures to safeguard AI workloads and ensure trust in AI-driven decision-making. The future of AI security depends on our ability to anticipate and address evolving threats in real time.

Want to learn more about AI workload security? Explore how Sysdig can help protect your AI environments with real-time detection, risk management, and compliance solutions. Download the ebook now.

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Article Link: 5 Steps to Securing AI Workloads | Sysdig