What Is AI Security?

AI security includes security controls designed to protect AI systems against common attacks. These include the manipulation or deletion of training data and the use of malicious prompts designed to access sensitive data or cause an AI system to misbehave.

As companies increasingly adopt and trust AI systems in important business processes, AI security is becoming extremely important. Successful attacks could cause sensitive data leakage, biased AI models, Denial of Service (DoS) attacks, or similar incidents.

AI systems face many of the same threats as traditional IT systems, but they also face risks unique to them. Implementing AI security requires understanding common risks to AI model integrity and data privacy and how best to address them.

Understanding AI Security

AI security focuses on protecting AI systems, models, and data from various threats. As these systems are increasingly permitted access to high-value data and workflows in industries like healthcare and finance, insecure AI could result in data breaches, biased models, or unauthorized access to corporate systems.

AI security is a complex and multidisciplinary field, requiring expertise in machine learning (ML), cybersecurity, and ethics. Ensuring the security of AI systems requires tailored security solutions, such as AI security posture management (AI-SPM).

Key Components of AI Security

AI systems are complex and multi-disciplinary, which exposes them to a wide range of potential threats. As a result, AI security encompasses a wide range of best practices addressing risk to data, models, access, and operations. While many of the key components of AI security are familiar from traditional cybersecurity, AI’s nature and use cases introduce new challenges.

AI Security Components

Data Protection

Data is critical to AI systems as they train their models and perform inference to accomplish various tasks. As a result, AI systems’ training data and internal parameters must be protected against unauthorized modification or deletion.

An attacker with access to this information could modify it to change how an AI model operates and the decisions it makes. For example, they might introduce mislabeled training data designed to cause an AI system to label malicious activities as benign and ignore them. Organizations can manage these risks by implementing data security best practices, such as access controls, encryption, data loss prevention (DLP), and data anonymization.

Model Integrity

AI models learn from their training data, compressing the information that it contains into a set of model weights. Attackers who can modify the model’s training data or weights can change how it operates. Alternatively, an attacker studying model outputs may be able to extract sensitive training data encoded within it.

Organizations should perform ongoing monitoring and validation of AI models to identify any potential changes in behavior that might indicate an attack or other issue. During the training process, the company should also perform adversarial testing to secure the model and adopt data privacy best practices, such as differential privacy.

Access Management

AI models are powerful systems with access to highly sensitive data, especially as companies adopt agentic AI. An attacker with access to these systems may be able to use them to further their attacks. For example, an attacker may be able to query an AI system to collect sensitive information or order the system to take malicious actions on another system.

Managing these risks requires strictly controlling access to AI systems and data. Organizations should implement least privilege access controls for users and AI systems alike and network segmentation, minimizing unauthorized access to data and systems. Additionally, multi-factor authentication (MFA) should be implemented for all accounts to reduce the risk of account takeover attacks.

Lifecycle Security

AI systems can be targeted by cyberattacks at any stage of their lifecycle, from initial training to end-of-life. During training, an attacker could inject malicious data into the training corpus, introducing biases into the resulting AI model. A deployed AI system may be targeted by prompt injection, model inversion, or similar attacks.

Organizations should implement security controls at every stage of the AI lifecycle, including secure CI/CD pipelines, version control, and regular auditing and logging. The practice of DevSecOps can also be extended to AI systems, ensuring that access controls, data security, and other best practices are built into the system from the start. Otherwise, organizations risk model drift, unmonitored shadow deployments, or data leakage during development.

API and Endpoint Security

AI systems are often exposed via APIs to allow integration into automated workflows. However, these interfaces can also expose these systems to unauthorized access and potential abuse if security controls are not in place.

Exposed, unsecured endpoints can allow prompt injection, system abuse, or unauthorized inference requests. Organizations should implement a zero trust security model and API security best practices, such as authentication via OAuth2.0, rate limiting, IP whitelisting, and encryption of requests and data in transit.

Securing Generative AI Models

Generative AI (GenAI) is a particular type of AI system that includes tools like ChatGPT, Claude, and others. These systems introduce additional potential risks, such as prompt injection attacks, model hallucinations, and data leakage. Organizations should implement input sanitization, access controls, and output verification to manage these risks to the business.

AI Security Challenges

AI security can be challenging for several reasons, such as:

• Lack of Transparency: Most major GenAI tools are unexplainable, meaning that it is infeasible to determine how and why a tool made a particular decision. This makes it difficult to identify bias and other potential errors in the system’s output.
• Third-Party Risk: AI systems and workflows commonly involve third-party data, models, integrations, and more. Errors or security flaws in these third-party components can introduce supply chain risks for the business.
• Evolving Risks: AI systems are rapidly changing and in their relative infancy, meaning that threats and potential attacks are not fully known. As a result, an organization may not be aware of the various risks associated with its AI systems and how to best address them.
• Regulatory Compliance: AI model training data commonly contains sensitive information, which may be exposed via data breaches or model inversion. These risks, as well as requirements for consent for processing, can complicate compliance with GDPR, CCPA, and similar laws. Organizations must also keep up with evolutions in the technology and regulatory requirements. 

AI Security in Practice

Organizations in various industries implement AI security best practices to manage potential risks to sensitive data and critical workflows. These include explicitly addressing AI security risks and deploying tools and technologies to prevent and detect potential attacks.

Examples

Many industries, such as finance and healthcare, are applying AI systems to highly sensitive data that is protected under various regulations. Some ways that they manage risk include:

• Data Classification: Data classification tools identify sensitive and protected data included in AI training datasets. This enables organizations to encode, mask, or otherwise anonymize data before feeding it into an AI model.
• Adversarial Input Detection: Inputs to AI systems may be analyzed before inference is performed. These monitoring systems may look for malicious inputs that are crafted to achieve a particular response.
• Output Monitoring: Organizations may also implement automated monitoring of the output of AI systems to look for potential anomalies. For example, an output that deviates significantly from the norm might trigger an alert that requires human review and approval. Businesses may also perform automated policy reviews of responses to protect against unauthorized access to sensitive data and similar risks.

Tools and Technologies

AI security employs many of the same security tools and technologies as traditional cybersecurity. For example, an organization may use an intrusion detection system (IDS) to identify various attacks and a security information and event management (SIEM) system for event correlation and analysis.

However, AI security also has its own tools and techniques. An organization may employ AI-SPM to manage the configuration of its AI systems or use AI-powered threat detection and prevention systems to block attacks targeting its AI systems.

FAQs about AI Security

Why is AI security important?

Organizations are increasingly allowing AI systems to access sensitive data and perform critical workflows. Securing these systems is critical to prevent data leaks, operational disruptions, and other potential threats to the business.

What are some common challenges in AI security?

AI security can be challenging due to the lack of transparency of AI systems and their reliance on third-party data and components. Additionally, organizations need to cope with an evolving threat landscape and regulatory requirements.

What are some best practices for implementing AI security in enterprise settings?

Organizations should implement least-privilege access controls to secure sensitive data and AI systems against unauthorized access. Also, inputs and outputs of AI models should be monitored for potential malicious requests or incorrect answers.

Strengthening AI Security with a Unified Network Strategy

AI security is critical to manage the potential threats that AI systems can pose to corporate data, systems, and workflows. Organizations should implement AI governance policies and security controls designed to address key elements of AI security, such as data protection, access management, and model integrity. Businesses should assess their existing AI security measures against evolving threats and regulatory requirements.