Welcome, in this article I discuss merging the rules of DevSecOps to help build more secure artificial intelligence solutions of the future.
DevSecOps, which combines development, security, and operations, can play a significant role in enhancing AI solution security. DevSecOps practices can be applied to help improve AI solution security overall.
Use Secure AI Model Development, which is, secure coding practices and security testing throughout the AI model development lifecycle. This will involve performing code reviews, combining both static code analysis (SAST), and dynamic security testing (DAST) to identify and address known vulnerabilities within the AI model codebase.
Application Security Testing, which is, automated security testing of the AI solution itself. Like the AI model, use static application security testing (SAST) and dynamic application security testing (DAST) of the application codebase. In addition perform software composition analysis to check for known vulnerabilities within dependencies.
The underlining infrastructure also needs to be assessed, use Infrastructure as Code to build the environment for consistency and repeatability. Implement security controls such as access restrictions, network segmentation, and secure communication within the IaC scripts. Store any credentials securely within encrypted stores only accessible to the build process and perform regular security scans of the Infrastructure as Codebase.
Securely deploy the solution for AI models, application and infrastructure. Ensure that proper access controls and authentication mechanisms are in place to prevent unauthorized access to these layers and any associated data and services this depends upon. All layers should implement encryption in transit and at rest with any deployment scripts secured to only those roles with responsible for deployment.
Perform vulnerability assessments and security monitoring for vulnerabilities in the running solution, responding to threat and vulnerabilities as they are detected using incident response processes. Perform frequent penetration testing on the AI infrastructure and solution within pre-preproduction fixing vulnerabilities as soon as possible in line with business priorities.
The AI solution should implement robust data protection controls to ensure the confidentiality, integrity, and availability of all solution data. This includes a combination of strong encryption, access controls, granular permissions and labelling.
Make sure the current solution is compliant for all laws and regulations to the countries it provides service to. Regular assessments of the regulations and laws are needed to ensure the solution is updated to remain compliant.
There is no set plan for DevSecOps, with each solution and development team differing by purpose and culture there will be variation. Look at the phases of development, look at the people, process and technology you have or can afford, balance with the time available. Catching the security issues early can save an organisation potentially in the worst case from bankruptcy and encourage a customer base that place trust in a solution that is more robust and secure. Good luck building your AI solution and let me know if this article helped you!
