Writer: Oluwafemi Kunle-Lawanson
As artificial intelligence (AI) becomes increasingly embedded in various industries, enhancing productivity and enabling new capabilities, it also introduces unique information security (InfoSec) risks. These risks span multiple facets, from data privacy and model vulnerability to operational dependencies on external cloud and API services. With these advancements comes the urgent need for effective risk management strategies to protect against data breaches, privacy violations, and unauthorized system manipulation. The National Institute of Standards and Technology (NIST) Cybersecurity Framework, a widely respected approach to cybersecurity risk management, provides a solid foundation for managing these risks. This article explores how to adapt the NIST Framework’s five core functions—Identify, Protect, Detect, Respond, and Recover—specifically for AI to manage these risks.
The first function in the NIST framework, Identity, involves thoroughly understanding AI assets and associated risks. Organizations must inventory their AI assets in this stage, including datasets, models, and third-party dependencies like APIs or pre-trained models. This step is crucial to uncover potential vulnerabilities and determine where data handling issues may arise. For example, sensitive data used in training models could expose the organization to data privacy risks if not managed carefully. Additionally, assessing compliance requirements, such as GDPR for personal data or HIPAA for healthcare information, helps align data practices with regulatory standards. By categorizing these assets and recognizing risks unique to each, organizations can establish a strong foundation for implementing effective security measures.
The Protect function is a cornerstone in securing AI systems from potential attacks. This includes setting strict access controls for sensitive data and model access, using role-based access control (RBAC) and multi-factor authentication (MFA) to prevent unauthorized access. Encrypting data both at rest and in transit is essential, especially when dealing with sensitive training datasets. Model training hygiene, which involves screening training data for inaccuracies or biases, is another protective measure that can prevent data poisoning—a technique where attackers inject malicious data to distort AI results. Finally, secure development practices, including using secure coding standards and implementing secure APIs, are critical in limiting exposure and minimizing the risk of exploitation. These measures reduce the chances of unauthorized access and manipulation, safeguarding data and model integrity.
The Detect function—detecting threats early is critical to maintaining AI system security. Anomaly detection tools can help establish a baseline of normal AI behaviour, such as expected response times and accuracy metrics. Deviations from these benchmarks may signal an attack or other issues. Data integrity checks are equally important, allowing organizations to monitor for signs of adversarial attacks or data poisoning that could compromise AI outputs. Log analysis also plays a vital role in monitoring AI interactions, especially for APIs and access to critical datasets. For AI models relying on external APIs or third-party libraries, regular third-party audits can help uncover vulnerabilities in the supply chain that may otherwise go unnoticed. By establishing these detection mechanisms, organizations can spot and address issues in real time, preventing further escalation of potential security threats.
The Respond Function – Responding effectively to incidents is vital in mitigating their impact. Organizations should establish a tailored incident response plan (IRP) for AI-related incidents, with specific procedures for model rollback, data recovery, and system isolation to contain issues promptly. A robust version control system for AI models allows for quick rollbacks in case of an incident, like an attack that causes model drift. Additionally, containment strategies are essential in preventing the spread of malicious activity, while clear communication protocols ensure that relevant stakeholders, including customers, regulatory bodies, and data providers, are notified. Organizations can swiftly contain incidents and restore system integrity with minimal disruption by crafting a response plan that explicitly addresses AI risks.
Finally, the Recover function emphasizes resilience and continuous improvement. Regular backups of datasets and model versions allow for quick recovery in case of data loss or system failure. Conducting post-incident reviews to understand the root cause and adapting security practices to prevent future incidents is essential for ongoing risk management. For AI systems, this may involve retraining models or modifying data sources to mitigate vulnerabilities uncovered during the incident. Transparency is also important; documenting and sharing recovery actions with stakeholders fosters trust and accountability. A well-defined recovery plan minimizes downtime and strengthens the overall resilience of AI systems, ensuring that security measures are continuously adapted to evolving threats.
Integrating a risk management mindset for AI requires an ongoing commitment to assessing and updating security measures. The NIST Cybersecurity Framework, tailored for AI, provides a structured approach that helps organizations manage risks effectively. However, this approach should also include specific AI best practices such as bias detection, transparency, and privacy-enhancing technologies to address ethical concerns and meet regulatory requirements. As the adoption of AI grows, the landscape of infosec risks will continue to evolve. Organizations that embed the NIST framework into their risk management practices are better positioned to protect sensitive data, prevent breaches, and foster trust in their AI-driven operations.
