Just when you thought you’d locked up government assets tightly against ransomware hackers—and could relax a bit—it’s time to worry about AI “poisoning.” We mean poisoning in the digital sense: the deliberate manipulation of AI models and data to that the models produce inaccurate or unreliable results. For government agencies, whose recommendations can have enormous public impact, the prospect of delivering wrong guidance or predictions is unnerving.

As the U.S. National Institute of Standards and Technology (NIST) reported in its recent paper about AI manipulation, adversaries want to target AI and machine learning systems to create real-world disruption, and they’re aware of the growth of AI technology in both the private and public sectors. The arrival of AI poisoning highlights the need to safeguard against malicious attacks on AI code, ensuring the integrity, reliability, and security of AI systems.

Minimize the risk and impact of AI poisoning

The time is now to get proactive about defending AI systems against poisoning, so your agency doesn’t end up in reactive mode where the damage has already been done. We’re keeping a close eye on this trend at Quantexa; below are the steps we believe should be employed to ensure the quality of AI data and models, while also protecting against attacks.

• Code review and testing. Conducting thorough code reviews and testing throughout the development lifecycle to identify and address potential vulnerabilities, loopholes, and security flaws in the AI algorithms and software.

• Secure development practices. Implementing secure coding practices and standards to minimize the risk of common security vulnerabilities such as injection attacks, buffer overflows, and authentication bypasses. This includes input validation, parameterized queries, and secure authentication mechanisms.

• Access control and authentication. Implementing robust access control mechanisms to restrict access to AI code, data, and infrastructure based on the principle of least privilege. Utilize strong authentication methods such as multi-factor authentication (MFA) to verify the identity of users and prevent unauthorized access.

• Data privacy and confidentiality. Implementing strong encryption techniques to protect sensitive data at rest and in transit. Utilize data anonymization and pseudonymization techniques to minimize the risk of data breaches and unauthorized access to personal or confidential information.

• Monitoring and logging. Implementing comprehensive monitoring and logging mechanisms to track access, usage, and changes to AI code, models, and data. Monitor for suspicious activities, unauthorized access attempts, and anomalies that may indicate a potential security breach.

• Patch management and updates. Keeping software and dependencies up to date with the latest security patches and updates to address known vulnerabilities and security weaknesses. Implement a robust patch management process to ensure timely deployment of security updates.

• Secure deployment and configuration. Following best practices for secure deployment, including network segmentation, firewall configuration, and proper configuration of access controls.

• Security training and awareness. Mandating security training and awareness programs for developers, data scientists, and other personnel involved in AI development and deployment to educate staff about evolving security threats, attack vectors, and best practices for secure coding and deployment.

• Incident response and contingency planning. Developing and regularly testing incident response plans and contingency measures to effectively respond to security incidents and mitigate their impact. Quantexa has robust protocols for reporting and addressing security vulnerabilities and breaches promptly.

• Third-party risk management. Conducting thorough security assessments and due diligence when working with third-party vendors, suppliers, or partners to ensure that they adhere to security standards and best practices to minimize the risk of supply chain attacks or vulnerabilities.

How to choose vendors that value security

When organizations purchase AI solutions, they also purchase those solutions’ security protections – or lack thereof. It’s worth quizzing vendors on how they secure business data and AI solutions to thwart criminals. The capabilities below are signs that solutions are taking security seriously.

• Adaptability to emerging threats. Solutions should be adaptable and responsive to evolving security threats and technological advancements in the field of AI.

• Collaboration and industry standards. Vendors should play a role in contributing to industry-wide security standards and best practices, fostering collaboration within the AI community to address common security challenges.

• Transparency and accountability. Choose vendors that are committed to transparency in their security practices and accountability in responding to and managing security incidents.

• Customer assurance. Vendors should inspire trust and confidence in their security measures and in the reliability and safety of their AI systems.

At Quantexa, we’re acutely aware of the need of proactive approaches for looming security threats in the government market. We’re not only enhancing the resilience of our AI systems, but we also ensure our customers are better protected against malicious attacks on AI code. We take steps to safeguard our systems from these threats, including thorough code reviews, secure development practices, and advanced data protection techniques. Learn more about Quantexa’s government solutions here.