The transformative power of Artificial Intelligence (AI) across industries is undeniable. However, with this power comes a critical responsibility – ensuring the secure deployment of AI systems. Secure deployment safeguards sensitive data, fosters trust in results, and mitigates the risks of misuse. This article delves into the National Security Agency’s (NSA) best practices for securing AI systems, empowering organizations to confidently leverage the full potential of AI in cybersecurity.

7 Best Practices for Deploying Secure AI Systems

1. Complexity and Resource Dependence: Deploying AI systems securely is complex and depends on the nature of the AI system, the resources available, and the type of infrastructure, whether on-premises, cloud, or a hybrid. Depending on these factors, different security protocols and measures need to be adopted. For instance, cloud deployments might need specific strategies to guard against risks unique to cloud computing environments.
2. Securing Proprietary Data Sources: To address risks like data poisoning or backdoor attacks, it’s crucial to keep a catalog of trusted and verified data sources. When utilizing third-party data, incorporating strong contractual or service level agreements can ensure compliance with security expectations. Applying secure by design principles and Zero Trust frameworks helps manage risks associated with the movement and processing of data in AI systems.
3. Hardening Deployment Environments: Consistent application of security best practices to the deployment environment is fundamental. This includes techniques like sandboxing machine learning (ML) models within hardened containers or virtual machines, monitoring network traffic, and configuring firewalls with specific allowlists. Regular review and updates of hardware and software also minimize risks from vulnerabilities.
4. Encryption and Key Management: To ensure the confidentiality and integrity of sensitive AI information like model weights, outputs, and logs, encrypting this information at rest is crucial. Using hardware security modules (HSM) to store encryption keys enhances security by enabling secure on-demand decryption.
5. Authentication and Phishing-Resistant MFA: Implementing strong authentication mechanisms and ensuring the use of phishing-resistant multifactor authentication (MFA) is essential for restricting access to sensitive information and services. This reduces the risk of security breaches through stolen credentials or phishing attacks.
6. Incident Detection and Response: Adopting a Zero Trust mindset assumes that breaches are inevitable or might have already occurred. Efficient and high-performing cybersecurity solutions are necessary to detect unauthorized access attempts promptly. Integrating an incident detection system allows organizations to prioritize incidents and react quickly, such as disconnecting compromised systems to limit damage.
7. Continuous Validation and Protection of AI Models: Before and during the use of AI systems, it’s important to validate their integrity using cryptographic methods and digital signatures. This helps ensure that the AI models and their components have not been tampered with and are operating as expected. Continuous validation helps protect the system against vulnerabilities, malicious code, or unintended properties.