Artificial intelligence systems are becoming central to the operations of countless organizations worldwide, driving innovation and efficiency across various sectors. However, this rapid integration also presents a significant AI systems security challenge. As AI technologies become more entrenched, they attract attention from cybercriminals aiming to manipulate or steal critical data.
The threat is not only from novel, AI-specific tactics but also from traditional cyberattack methods, now repurposed to target these advanced systems. The complexity of these threats requires that organizations adopt a multifaceted approach to security, blending established IT defense strategies with new, AI-specific protections.
Amidst the threats of cyberattacks on AI systems, the National Security Agency (NSA) has released a comprehensive guide, “Deploying AI Systems Securely: Best Practices for Deploying Secure and Resilient AI Systems.” This report is a blueprint for enhancing the security posture of AI systems. Below, we explore the NSA’s latest recommendations for improving the security of AI systems to fortify defenses and ensure safe, reliable operations.
Recommendations for Securing the Deployment Environment
Organizations must prioritize creating a secure deployment environment when integrating AI technologies into existing IT infrastructures. The NSA recommends implementing the following strategies to create a secure deployment environment:
Governance of the Deployment Environment
Organizations should collaborate closely with IT departments to align the deployment environment with overall IT standards. This includes evaluating the risks associated with the AI system, understanding the organization’s risk tolerance, and ensuring that the AI deployment does not exceed these thresholds. It is crucial to establish clear roles and responsibilities for all stakeholders, especially if the IT environment and AI systems are managed separately.
Architectural Integrity and Security
The NSA recommends that organizations implement protective measures at the boundaries between the AI system and the IT environment to prevent unauthorized access and data breaches. This involves identifying security gaps that attackers could exploit and applying principles such as zero trust and secure-by-design to manage and mitigate risks effectively. Protecting proprietary data sources used in AI model training is also paramount to preventing data poisoning and other forms of attacks.
Configuration and Network Security
Proper configuration of the deployment environment is essential to fortify it against potential threats. This includes implementing strict network monitoring and firewall configurations to control traffic and detect anomalies.
Encrypting sensitive AI information, such as model weights and outputs, adds a security layer, ensuring that data is protected at rest and during transmission. Organizations should also deploy strong authentication mechanisms and access controls to manage who can access the AI system and respond promptly to any signs of fraudulent activity.
Proactive Threat Management
Adopting a zero-trust mindset, which assumes that breaches are not only possible but inevitable, is crucial for maintaining security over time. Organizations must enhance their incident detection and response capabilities to quickly address and mitigate security breaches or attacks. This includes using advanced cybersecurity solutions to monitor for unauthorized access attempts and integrating systems that can prioritize and manage incidents effectively.
Recommendations for Sustaining AI System Security
Here, we detail essential ongoing protection measures recommended by the NSA for safeguarding AI deployments:
Verification and Integrity Checks
Organizations should employ cryptographic methods, digital signatures, and checksums to verify the origin and integrity of all components involved in AI processes. Creating encrypted backups of AI models and storing them in secure, tamper-proof locations is essential for preventing unauthorized access.
Robust Testing and Rollback Procedures
It is important to rigorously test AI models for accuracy and attack vulnerability, including adversarial testing to assess resilience. Organizations should also prepare for automated rollbacks to revert to stable states during security incidents or when updates compromise system integrity.
Automated Security Measures
Automating the detection, analysis, and response to security incidents can enhance the efficiency of IT and security teams. Continuous monitoring of AI models and their environments helps detect and address potential security issues promptly. This includes using AI capabilities to streamline automation while maintaining necessary oversight through human-in-the-loop systems.
API and Model Behavior Monitoring
If AI systems expose APIs, organizations should secure these interfaces with strong authentication and authorization measures. Implementing input validation and sanitization can prevent the exploitation of these interfaces. Additionally, they should actively monitor all aspects of AI model behavior—from data inputs and outputs to system configurations—to detect and respond to unauthorized changes or attempts to access sensitive data.
Protecting Core Assets
Firms should give special attention to protecting model weights, which are critical to AI functionality. They should harden access interfaces and use hardware-based security measures to safeguard these assets. They should also isolate storage areas to enhance security and use measures such as hardware security modules(HSMs) for enhanced protection.
Recommendations for Ensuring Secure Operations and Maintenance of AI Systems
Organizations must adhere to rigorously defined processes and procedures to maintain the integrity and security of AI systems. Below are key strategies for securing and preserving AI system operations:
Implementing Strict Access Controls
Organizations should enforce strict access controls to protect AI models from unauthorized access or tampering. By applying role-based or, preferably, attribute-based access controls, they limit system access to authorized personnel only. Additional measures such as multi-factor authentication (MFA) and the use of privileged access workstations (PAWs) are vital to differentiate between user levels and secure administrative activities.
Promoting Security Awareness and Continuous Training
It is essential to continuously educate users, administrators, and developers on security best practices, including strong password management and phishing prevention. Cultivating a security-aware culture reduces the risk of human error, which can be a significant vulnerability in AI system security.
Routine Audits and Penetration Testing
Engaging external security experts to conduct regular audits and penetration testing is a proactive approach to identifying potential vulnerabilities within AI systems. These checks are critical for uncovering issues that might not be immediately apparent to internal teams.
Regular Updates and Patch Management
Organizations should regularly update and patch AI systems, conducting thorough evaluations of the model’s performance and security post-update to ensure that all aspects function within acceptable parameters.
High Availability and Disaster Recovery Preparedness
Preparation for high availability and effective disaster recovery involves using immutable backup storage systems. These systems ensure that critical data, such as log files, cannot be altered or deleted, which is essential for recovering from catastrophic events.
Secure Deletion Protocols
Secure deletion capabilities are critical for removing sensitive data permanently. Organizations must have mechanisms in place for the autonomous and complete deletion of sensitive components like training models or cryptographic keys at the end of their lifecycle or after their use is concluded. The goal is to eliminate any data remnants that cybercriminals can exploit.
Adopt a Proactive Stance on AI Security With TeraDact’s Products
Securing AI systems is a complex, evolving challenge that requires dedicated attention and specialized knowledge. The NSA’s comprehensive guidelines recognize this and offer not only practical steps for securing AI deployments but also emphasize the importance of continuous improvement and adaptation to the rapidly changing cybersecurity environment. By implementing these best practices, organizations can safeguard their AI systems against increasing threats.
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