What is AI Workload Security?

AI workload security definition

AI workload security is the ongoing protection of tasks and processes — performed by AI models, systems, and tools in the workplace — from risks, threats, and attacks. This aspect of AI security involves protecting AI models from manipulation, data exposure, adversarial attacks, and more.

By adopting AI workload security, organizations get visibility into where employees are using AI models and systems. This helps to maintain data integrity and enable organizations to feel confident that AI usage follows security policies and compliance regulations.

While some organizations are developing their own AI models, the majority rely on third-party platforms like Anthropic and OpenAI. AI workload security provides visibility needed to understand where risk exists and prevent or mitigate attacks on API integrations and AI pipelines.

Organizations need to understand that AI isn’t something completely new for security teams to protect. It’s just more software running on specialized hardware. AI models are built on the same cloud-native and containerized infrastructure that security teams have been protecting for years.

What is an AI workload?

To understand AI workload security, it’s important to understand what constitutes an AI workload. An AI workload is the collection of tasks and processes used to run and teach AI models and systems.

Workload types include data processing, model training, model inference, analytics and monitoring, and specialized tasks like natural language processing.

AI workloads differ from cloud workloads because of the increased complexity and compute resources required. Protecting both types of workloads is an essential part of cloud security.

Why is AI workload security important?

More and more organizations are adopting AI in the workplace, opening up new attack surfaces if not protected properly. Sysdig found that 34% of all currently deployed generative AI workloads are publicly exposed.

AI workload security is needed because organizations must understand where AI is being used in the workplace and what potential risks exist because of its usage. Additionally, the shared responsibility model places the security burden on organizations and not vendors when using third-party AI models.

It can also help organizations comply with current and future AI governance requirements, which remain in flux as countries decide how best to implement regulations. For instance, China is working on a centralized approach. The U.S. government will follow suit after President Trump signed an executive order preventing a decentralized, state-independent approach that a 2023 executive order directed.

AI introduces new security threats and risks that organizations need to understand and protect against. This includes:

• Data poisoning. Attackers compromise an AI model’s accuracy and reliability by introducing malicious data into the training pipeline.
• Model inversion. Attackers attempt to determine the training data and other sensitive information based on model outputs through repeated queries.
• Adversarial attacks. Attackers manipulate AI models into making wrong decisions to compromise trust and decision-making capabilities.
• API vulnerabilities. Attackers target APIs that integrate with AI models to expose systems and perform injection attacks.

Lastly, customers need to be able to trust that AI models work as intended and their data remains secure even when using it. AI workload security ensures customers feel confident and satisfied in the results from AI models.

Benefits of AI workload security

Organizations are still learning how to best protect AI workloads in the enterprise. Implementing AI workload security includes the following benefits:

• Prepared for future regulations. With AI workload security in place, organizations are better prepared to comply quickly with current (EU AI Act) and future regulations since they’ll know what workloads they have, where they are, and the risks associated with them.
• Secure AI models based on risk prioritization. Using runtime insights, security teams can identify in-use AI packages and protect them based on risk prioritization.
• Gain visibility into AI model and tool usage. With the proper AI workload security solution, organizations can discover where AI use exists and then determine whether it’s sanctioned or not.

Challenges of AI workload security

With AI models and their use still evolving, it becomes difficult to understand how and where security teams should apply measures and controls. Some AI workload security challenges include:

• Shadow AI usage. Shadow IT is common and applies to AI, too. Expect that employees will either use sanctioned AI models inappropriately or use ones not yet approved. Organizations need to adopt AI security tools with the ability to discover and monitor approved and unapproved AI tools.
• Opaque view into AI pipelines. Third-party AI models may lack transparency in an effort to protect proprietary or sensitive intellectual property, which adds to the challenge of knowing how to effectively secure the tools. Using an AI bill of materials (AIBOM) can shed light on AI components and illustrate where IP begins and ends.
• Understanding AI-specific risks. Securing AI will initially feel like the same methods for protecting any other asset or dataset, but AI models come with additional risks that security teams need to understand to ensure proper controls are in place. This includes model poisoning, model drift, model inversion, and more.

How AI workload security works

AI workload security starts with providing visibility into where AI packages currently exist within a cloud environment. This helps determine and understand where active risks and vulnerabilities exist within AI models, pipelines, APIs, and other systems. Security teams need to understand where AI packages run in the workplace to help prevent data exposure and other AI threats.

From there, AI workload security tools continuously monitor and analyze for risks and their context to determine prioritization. Context includes the possibility of public exposure and misconfigurations, suspicious activities, and vulnerabilities in active AI packages.

AI workload solutions also help surface critical attack paths targeting AI packages that involve vulnerabilities, misconfigurations, and runtime detections. By understanding potential threats and risks, security teams can prevent potential attacks and thwart active ones from stealing data or progressing further.

Best practices for AI workload security

When developing an AI workload security program, organizations should consider how to protect AI environments without stifling innovation.

Here are five AI workload security best practices to implement:

1. Create a complete inventory of AI environments. Build a comprehensive inventory of all AI models and deployments, and track and secure the data each model accesses by auditing model permissions regularly.
2. Harden security posture to reduce risks. Reinforce AI environments with least-privileged access to allow only approved users and machines access to models and systems. Implement risk-based vulnerability management to scan and prioritize risk based upon business impact. Use cloud security posture management (CSPM) to secure configurations and extend governance to models.
3. Implement real-time detection and response. Adopt real-time detection and response to get runtime visibility to spot anomalous behavior; correlate signals from cloud services, containers, and AI systems to detect attacks; finally, automate containment actions.
4. Build resilience through a combination of prevention and detection. Alongside real-time detection, implement prevention methods to secure AI systems. Do this by sharing workflows between security, cloud, and AI teams, review threat insights, and review incidents regularly to understand security shortcomings.
5. Regularly assess security measures as AI usage evolves. Review and update AI security as data pipelines expand and new integrations are added to ensure visibility remains into AI models and systems, and update security controls as needed.

Secure AI workloads with Sysdig

AI adoption will only continue to grow, and this new attack surface could leave organizations vulnerable to threat actors.

To help protect AI workloads, Sysdig offers an AI workload security solution that provides real-time monitoring and detection, centralized visibility, scalable architecture, and compliance and policy enforcement.

Sysdig’s AI workload security provides a unified dashboard to understand where AI workloads are running, potential risks, and risk prioritization. Stay ahead of threat actors and AI regulations by getting visibility into AI workloads, their locations, and their risks.

Learn more about AI workload security and how Sysdig can help by downloading our ebook here.