The Next Frontier: Integrating Threat Modeling into Machine Learning Systems

Threat Modeling in the Age of Machine Learning

As organizations increasingly incorporate machine learning (ML) into their software applications, the need for threat modeling to identify security flaws in design has become paramount. Threat modeling enables organizations to proactively address security risks, such as data poisoning, input manipulation, and data extraction, in ML systems. By understanding and mitigating these risks early in the development lifecycle, organizations can reduce the time spent on security testing before production.

According to Gary McGraw, co-founder of Berryville Institute of Machine Learning, there has been a lot of discussion about ML risk, but the challenge lies in figuring out how to address these risks. Threat modeling, which involves identifying potential threats to an organization, helps in thinking through security risks specific to ML systems. It allows developers and security teams to look for design-level security issues, as recommended by the National Institute of Standards and Technology (NIST) Guidelines on Minimum Standards for Developer Verification of Software.

The Role of IriusRisk’s Threat Modeling Tool

IriusRisk, a leading provider of threat modeling solutions, has developed a tool that automates both threat modeling and architecture risk analysis. With this tool, developers and security teams can import their code to generate diagrams and threat models, making the process accessible even to those unfamiliar with diagramming tools or risk analysis.

Furthermore, IriusRisk has recently launched the AI & ML Security Library, which facilitates the threat modeling of ML systems. The library is based on the BIML ML Security Risk Framework, a taxonomy of ML threats and an architectural risk assessment of typical ML components developed by Gary McGraw. By integrating IriusRisk’s library into their workflow, organizations gain visibility into their ML usage and can effectively analyze and secure their ML systems.

Addressing Risks in Machine Learning

The AI & ML Security Library in conjunction with IriusRisk’s threat modeling tool helps organizations ask essential questions to identify and mitigate risks in ML systems. Some of these questions include:

• 1. Data Source: Where does the data used to train the ML model come from? Is there a possibility of embedding incorrect or malicious data?
• 2. Continuous Learning: How does the ML system keep learning once it is in production? Is there a risk of the system learning objectionable information that may require taking the system offline?
• 3. Confidentiality: Can confidential information be extracted from the ML system? How can organizations ensure that sensitive data remains protected?

By considering these questions early in the development process, organizations can proactively identify risks and implement appropriate control measures to safeguard their ML systems.

Benefits and Recommendations

The incorporation of threat modeling, particularly with specialized tools like IriusRisk, brings significant benefits to organizations using ML. By proactively addressing security risks during the design phase, organizations can save time on security testing before production. Furthermore, a mature threat modeling program, coupled with ML-specific solutions, enables organizations already engaged in threat modeling to better handle ML risks.

Recommendations for organizations that have yet to adopt threat modeling include starting the practice as part of their software design process. Threat modeling is not a new concept, and with the increasing integration of ML technologies, it is more crucial than ever to consider security risks from the outset. Organizations should also be aware of potential shadow ML, where individual departments may be utilizing ML applications or tools without the knowledge of IT and security teams. Gaining visibility into ML usage is essential for comprehensive threat modeling and risk mitigation.

Conclusion

As organizations embrace machine learning in their applications, threat modeling becomes vital for identifying and addressing security risks. The automated threat modeling tool offered by IriusRisk, coupled with the newly launched AI & ML Security Library, provides organizations with the means to proactively analyze and secure their ML systems. By understanding the unique risks associated with ML and taking appropriate mitigation measures, organizations can stay ahead of potential threats and maintain the integrity of their systems.