Introduction to Securing AI Applications
Security teams face significant challenges when AI applications are moved into production, including securing, monitoring, and defending them. To efficiently and effectively incorporate AI applications into the operational security workflow, security teams must follow a set of practices. In this article, we will discuss 12 ways security teams can take control of AI applications in production.
12 Practices for Securing AI Applications
The 12 practices for securing AI applications include:
- Visibility: continuous visibility into AI applications to identify exposures of sensitive data, vulnerabilities, and other issues.
- Understand Risk: using data to scientifically understand risk, rather than relying on guesswork.
- Build Trust: building relationships between the security team and other stakeholders, such as application owners and developers.
- Leverage Trust: involving the security team earlier in the software development life cycle (SDLC) to facilitate the incorporation of AI applications into the operational security workflow.
- Telemetry: generating telemetry data from the AI layer, API layer, and application layer to provide visibility into the application and its infrastructure.
- Process: developing processes and procedures around securing AI applications to provide guidance to the security team.
- Enforce: ensuring the security team has the ability to easily implement and enforce controls across different environments.
- Preventive Controls: implementing good preventive controls to protect against abuse, fraud, DDoS, and other threats.
- Detective Controls: using continuous security monitoring to detect and respond to security issues.
- Investigation: analyzing and investigating data to respond to security issues.
- Mitigation: responding, remediating, and recovering from security issues.
- Iterate: continuously improving the security team's approach to securing AI applications through lessons learned and documentation of findings.
Importance of Visibility and Risk Understanding
Visibility and risk understanding are fundamental building blocks for securing AI applications. Continuous visibility provides insight into exposures of sensitive data, vulnerabilities, and other issues, while risk understanding enables the security team to scientifically evaluate the risk presented by AI applications.
Building Trust and Leveraging Trust
Building trust between the security team and other stakeholders is crucial for the successful incorporation of AI applications into the operational security workflow. Leveraging this trust enables the security team to involve themselves earlier in the SDLC, facilitating the incorporation of AI applications into the workflow.
Telemetry and Process
Telemetry provides visibility into the application and its infrastructure, while process provides guidance to the security team. Ensuring that telemetry data is thoroughly generated and that processes are developed and followed is essential for securing AI applications.
Enforce, Preventive Controls, and Detective Controls
Ensuring the security team has the ability to easily implement and enforce controls, implementing good preventive controls, and using detective controls are all critical for securing AI applications. These controls enable the security team to protect against threats, detect and respond to security issues, and respond to security incidents.
Investigation, Mitigation, and Iterate
Investigating security issues, mitigating their impact, and continuously improving the security team's approach through lessons learned and documentation of findings are all essential for securing AI applications. These practices enable the security team to respond to security issues, recover from incidents, and improve their overall security posture.
Conclusion
In conclusion, securing AI applications in production requires a set of practices that include visibility, risk understanding, building trust, leveraging trust, telemetry, process, enforce, preventive controls, detective controls, investigation, mitigation, and iterate. By following these practices, security teams can efficiently and effectively incorporate AI applications into their workflow and improve their overall security posture.
Source: SecurityWeek