Recent advances in machine learning, generative AI, and large language models have sparked widespread interest and investment across various industries, as businesses recognize the potential for these technologies to transform daily operations and enhance societal experiences. The failure to embrace AI innovations could result in companies becoming obsolete within their fields.
However, while AI offers numerous benefits, it also presents challenges that could negatively impact organizations. According to PwC’s 27th annual Global CEO Survey, conducted with 4,702 chief executives and released in January 2024, the majority of participants believe that generative AI (GenAI) offers more advantages than risks. Nonetheless, 64% of CEOs expressed concerns about potential cybersecurity threats that could arise from integrating AI into their operations.
To address these anticipated risks, industry experts recommend that organizations prioritize AI threat modeling when developing and implementing new AI systems and applications. This process involves identifying potential threats, establishing prevention and mitigation strategies, and integrating these considerations from the earliest design stages through the software development lifecycle.
For effective AI threat modeling, OWASP suggests a four-step, four-question methodology. Firstly, organizations should assess the scope of the AI threat model, outlining the AI system or application’s structure to pinpoint security vulnerabilities and potential attack vectors. It is crucial to identify and categorize digital assets accessible through the system, determine user access levels, and prioritize data, systems, and components based on their importance and sensitivity to the business.
Next, organizations must identify potential AI security threats and rank them according to their likelihood and potential impact. This may involve brainstorming sessions or structured approaches utilizing frameworks such as STRIDE, which categorizes threats into spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege. Furthermore, businesses must evaluate the broader AI threat landscape and understand the attack surface unique to their systems.
In the context of emerging large language model (LLM) threats, organizations must be vigilant against prompt injection attacks, data poisoning schemes, and AI model theft. Prompt injection attacks involve manipulating AI models into producing harmful outputs, while data poisoning manipulates training data to compromise AI intelligence. Model theft poses risks associated with unauthorized access to proprietary enterprise AI systems and data.
Following threat identification, organizations should define appropriate AI threat mitigation countermeasures. These strategies may involve deploying security controls to reduce risks, transferring risks to third-party providers, or accepting risks with minimal business impact. Each threat requires tailored security measures, ranging from access management protocols to system-level controls that prevent unauthorized changes to critical settings.
Ultimately, businesses must assess the effectiveness of their AI threat modeling efforts and document key findings for future reference. By following comprehensive AI threat modeling practices, organizations can proactively safeguard their AI systems, applications, and data from potential cyber threats and ensure continued operational resilience in an increasingly AI-driven world.