Hacking generative AI: Limiting security risk in the age of AI

Generative AI presents a new attack surface for hackers, with many organizations rushing to implement it without adequate security measures. Experts recommend a shared responsibility model for security, including thorough risk assessments, threat modeling, and penetration testing, as the skills gap in AI security is substantial and the potential for AI-related attacks is significant, including adversarial attacks that manipulate AI models to produce harmful outputs.

New AI attack surface

• 00:00:05 Generative AI introduces a new attack surface, requiring security professionals to constantly adapt and understand how it works to mitigate risks. Many companies are rapidly integrating AI without proper risk assessments, leading to vulnerabilities like weak authentication and code execution in production environments. This haste can expose enterprise data lakes and other sensitive information.

AI security skills gap

• 00:07:35 There's a significant skills gap in AI security, with many claiming expertise without sufficient experience or training. This creates a need to upskill security professionals and make developers and data scientists aware of AI-related security risks. The industry requires qualified personnel to address these emerging security threats.

Shared responsibility for AI security

• 00:08:38 AI security relies on a shared responsibility model between developers, data scientists, and AI-as-a-service vendors. It's not sufficient to simply rely on AI firewalls or guardrails; secure coding practices and thorough testing are crucial. Organizations must also understand the AI supply chain and ensure vendors provide sufficient testing and transparency about their solutions.

Common mistakes in AI security

• 00:14:27 Many organizations are not prioritizing security for their AI initiatives, with only 24% of current projects incorporating security components. Executives often prioritize innovation over security, potentially leading to vulnerabilities. Neglecting security fundamentals and an immature security culture leave organizations unprepared for new forms of threats, including malware and social engineering amplified by AI.

Recovering from rushed AI adoption

• 00:16:03 Organizations that have rushed AI implementation without adequate security measures should conduct a comprehensive security audit and risk assessment, ideally pausing deployment if possible. Threat modeling and penetration testing are essential to identify security gaps and simulate potential attacks. Evaluating data handling practices, like access control and encryption, is also critical. A detailed report with recommendations for improvement is crucial to help organizations improve AI security.