What is AI Security?

AI security definition

AI security has two different aspects: AI for security and securing AI. On one side, AI security, which can also be called AI-powered cybersecurity, is the use of artificial intelligence and machine learning to better secure data, applications, and systems. AI security can also mean AI workload security, which involves defending AI models and machine learning tools from threat actors during their entire lifecycle.

Though initially confusing, organizations need to consider both sides of the equation. AI can help speed up security measures, while securing AI workloads is important due to the proliferation of AI technology for better business workflows.

To keep things simple, we’ll start by discussing AI for security before breaking down how to secure AI.

Understanding AI for security

AI models and tools have progressed to the point where they can help strengthen an organization’s security posture. Human defenders need to fight back against attacks that can occur in seconds, especially if it is an attack boosted by AI. AI security tools can help with risk categorization, prioritization, and evidence gathering.

One option for organizations to use is agentic AI. This emerging type of AI tool can perform autonomous actions to achieve specific goals and tasks. Agentic AI can analyze situations and break down complex problems into more manageable tasks to achieve objectives. 

In security, agentic AI can be used to autonomously monitor a cloud environment by gathering data about tools, systems, and processes in place to determine risk prioritization. With this data, it can understand which risks will hurt an organization and which ones can be dealt with later. Agentic AI then surfaces the risks to security teams, providing context around them and suggestions on how to remediate each one.

Why AI for security is important

Keeping up with attackers isn’t easy. Attacks are becoming more sophisticated and threat actors use AI – called dark AI – to spin up new attacks in seconds. Cloud environments are also growing more complex, especially as organizations expand into hybrid or multi-cloud deployments. The attack surface grows by the day and human defenders can’t manage by themselves.

AI security can step in and help security teams move faster. It speeds up the time to discover incidents or risks. It can analyze mountains of data much quicker than human defenders and find suspicious patterns that might point to an attack. It can help reduce alert fatigue, while autonomously performing more repetitious tasks to lighten a security team’s workload.

Benefits of AI in security

• Improves incident response time: AI security can collect data from cloud tools to provide a comprehensive report on an incident and speed up mean time to resolution (MTTR). 
• Reduces alert fatigue: Not every risk needs to be immediately solved. AI can limit alerts to critical ones that need the security team’s attention and backburner anything else.
• Strengthens threat detection: AI can sort through mountains of data and find suspicious patterns, which human defenders might miss or misinterpret.
• Automates rote security tasks: Human defenders can have AI perform the more rote or mundane tasks in cybersecurity, allowing security teams to focus on more strategic security initiatives.
• Speeds up priority setting each day: Security operations center analysts can use AI prompts to start their day quickly.

Challenges of AI for security

AI security is not without numerous challenges that organizations must address to ensure the AI model and tools work as intended and don’t create unexpected roadblocks.

Some AI security challenges include:

• Data set training: AI is only as good as the data it has access to and is trained upon. A solid data foundation enables the AI to understand different attack patterns and how it can respond.
• Bias and discrimination: If the data set is flawed and includes intentional or unintentional bias, then the AI could misidentify future incidents or incorrectly attribute benign behavior as threats.
• Black box behavior: Without adequate transparency, AI models could be flawed and lead to wasted time and a lack of trust from human defenders.
• Regulatory compliance: Organizations need to navigate regulatory compliance issues when adopting AI for security. If not done properly, they could face expensive penalties.

AI for security best practices

To ensure AI for security helps, organizations should follow some AI security best practices, which include:

1. Develop AI security framework: Develop a structured framework for how AI for security is built and implemented to ensure it can help with risk management and threat detection more effectively.
2. Apply MLOps to AI usage: Use machine learning operations to assist in developing AI tools for security to prevent model drift and ensure effective version control.
3. Ensure transparency: The AI model cannot be a black box for security teams as they need to be able to understand its actions and trust that it’s working correctly.
4. Include human oversight: AI models cannot exist independently forever and need oversight to ensure they still work as intended.
5. Test AI models to keep them secure from outside influence: Train AI models for security to be resilient against potential threats like data poisoning, backdoor attacks, and model exploitation.

The future of AI security: Agentic AI 

AI security is still nascent, but many of the tools available don’t quite offer AI for security. Rather, they are little more than chatbots connecting defenders to documentation, static learning models, or limited scripted playbooks. Agentic AI changes that.

With agentic AI cloud security, organizations can shift from reactive to proactive. Agentic AI reduces alerts and provides reasoning and context behind risk triage to keep security teams aware of issues without drowning in low-risk noise.

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Securing AI: AI workload security

Now it’s time to look at securing AI, which can also be called AI workload security. Organizations need to understand how to secure AI models and components from attacks and vulnerabilities. Organizations need to be smart and methodical when deciding where and how to add AI capabilities. Haphazardly adopting AI workloads without proper controls in place can result in unintended security risks.

Why security for AI is important

Securing AI is important because AI models connect to critical data sets that could compromise an organization if exposed externally. Organizations need to know that any data entered into an AI model will be secure and protected. The training data sets will include both sensitive information as well as intellectual property, adding another potential attack surface.

AI security must also factor in how to keep the AI model away from external influence, where threat actors could manipulate it to either expose business-critical data or perform incorrect or malicious actions. Model tampering can render the AI tool unusable and erodes trust in AI.

Benefits of securing AI

The AI workload security benefits include:

• Improving trust in AI models and systems: Organizations need to know they can trust the output of their AI model and that it is free of data poisoning and other adversarial AI.
• Preventing data leakage: Implement strong data storage to prevent any training data sets or data that is accessible via AI from data exfiltration.
• Staying compliant with regulations: Strong security for AI helps organizations comply with data governance laws, especially in more regulated industries.
• Customer trust in AI models: Strong security ensures customers feel comfortable sharing sensitive information with an organization’s AI model and trust that it will stay protected.

Challenges of securing AI

Keeping AI workloads secure is not without its difficulties. Some AI security risks include:

• Adversarial AI usage: Threat actors employ a variety of AI attack vectors, like model inversion attacks, data poisoning, and prompt injection, to steal data or render the AI useless.
• Supply-chain complexity: Understanding where organizations need to keep AI models secure isn’t always easy or obvious. Security teams must contend with potential points of failure and prepare accordingly.
• Regulatory compliance: Many regulations are in their infancy or not yet released, making it difficult to understand data regulation expectations.
• Model drift: AI model’s performance can degrade over time and drift from training data sets. Monitor for drift and retrain to re-establish quality model performance.
• Identifying unintentional errors vs. manipulation: Look for accidental incorrect data input, which will impact how well the model works alongside manipulation efforts.

Security for AI best practices

Here are a few AI security best practices to keep models, datasets, and APIs protected:

1. Create an AI bill of materials: AIBOMs, much like software bill of materials, help organizations track every component, dependency, and dataset connected to the AI model to make it easier to know where to remediate critical CVEs.
2. Implement model hardening: Expose AI models to cyberattack examples to improve its resilience against threats.
3. Secure AI pipelines: Use security controls to protect the AI model during its lifecycle, from data collection to training to deployment.
4. Protect APIs and endpoints: Ensure that any APIs and endpoints that connect to AI systems are secure through layered defense.
5. Conduct regular audits: Know what data is used to train AI and log any changes to AI models. Look out for model drift and other issues.
6. Use OWASP Top 10: The Open Web Application Security Project released a list of 10 areas to secure large language models against. 
7. Use strong access controls: Secure data sets, APIs, and endpoints that connect to AI models with identity and access management controls, like role-based access control and least-privileged access.
8. Utilize rate limiting: Keep the amount of allowed queries limited to help protect against model inversion attempts.
9. Follow regulatory requirements: Align security measures with AI-focused frameworks, like NIST AI Risk Management Framework and ISO/IEC 42001.

Keep AI workloads secure with Sysdig

AI security is important at this juncture, where organizations are still learning how and where to add AI and machine learning in workflows. Adversarial AI and other AI threats can undermine trust in AI models and erode progress in a customer’s willingness to adopt AI.

Sysdig can provide AI security that includes comprehensive monitoring of AI models for resource usage and anomalies that point to malicious behavior, runtime security that identifies suspicious behavior and unauthorized access, and compliance enforcement to keep AI models with industry regulations and standards.

Sysdig AI workload security provides organizations with complete visibility and end-to-end security for workloads and training data sets.