Guest Jerich Beason, CISO, WM, discusses the risks of generative AI and the need for understanding, prioritizing safety, and adapting to its transformative nature. The chapter also explores building a trusted security framework and the challenges and evolution of AI.
Applying lessons from past technological advances can help mitigate risks associated with generative AI.
Creating separate policies for public and private AI models is crucial to protect intellectual property and build trust with AI.
Deep dives
Understanding and Managing Risk with AI
Managing risk with emerging technologies like AI can be challenging, as the risks are not always clear. It is important to apply lessons learned from past technological advances to mitigate risks with AI. This involves considering use cases, creating an AI policy, and prioritizing risk management. Additionally, organizations should explore both public and private AI models, as the latter poses additional risks and requires protection of intellectual property. Building a culture of trust and transparency, along with continuous education and awareness of AI-related threats, can help organizations navigate the risks associated with AI.
The Need for AI Policies
Having an AI policy is crucial to guide the appropriate use of AI and define ethical and moral standards. Organizations should consider creating separate policies for public AI consumption and private AI models. The policy should outline the approved use cases and provide measures to mitigate risks associated with building and protecting AI models. Protecting intellectual property, mitigating model theft, data leakage, and ensuring ethical use are key considerations for securing privately built AI models. Transparency and standardization are also vital in building trust with AI.
The Challenge of Building AI-Resistant Verification and Combatting AI-Based Attacks
As AI capabilities grow, there is a need for AI-resistant verification to detect AI-based attacks. While the process is challenging, organizations should focus on building robust infrastructure and processes that can withstand both human and AI-driven attacks. Additionally, as AI-based attacks become more prevalent, there is an opportunity for startups to emerge and combat these attacks using AI-based defense strategies. The future of cybersecurity will likely involve AI-versus-AI cyber duels, emphasizing the importance of continuously adapting and leveraging AI to stay ahead in the ongoing AI arms race.
Embracing AI Transformation with Responsible Governance
AI is a transformative technology that organizations should embrace while also practicing responsible governance. Understanding how existing large language models (LLMs) are being used is the first step in leveraging AI effectively. Developing AI policies that define use cases, enforcement measures, and risk mitigation strategies is crucial. Organizations should prioritize security, compliance, transparency, and ethical considerations. Emphasizing user education, awareness, and training can also reduce human-induced vulnerabilities. The future of predictive cybersecurity and using AI tools for real-time interception of malicious activities, such as phishing, shows promise in proactive threat detection and prevention.
How can we address these risks to take advantage of its benefits?
How do we approach a much desired technology we're not so sure how we should secure?
How can we take what we've learned from past technological advances and apply it to mitigate risks with generative AI?
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