HN754: Secure AI by Design with Palo Alto Networks (Sponsored)
Oct 18, 2024
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Rich Campagna, Senior Vice President of Product for Network Security at Palo Alto Networks, dives into the pressing security risks posed by generative AI. He discusses the critical importance of safeguarding employee productivity while navigating third-party AI tools. The conversation highlights challenges within healthcare AI applications, particularly around patient data privacy. Campagna also emphasizes the urgency for organizations to adapt their security frameworks to combat sophisticated cyber threats, advocating for a 'Secure AI by Design' approach.
The increasing use of generative AI tools introduces significant security risks that enterprises must evaluate to protect sensitive data.
Organizations face evolving challenges similar to past shadow IT issues, requiring updated strategies for risk management with user-driven AI adoption.
Cybercriminals leverage generative AI to enhance attacks, emphasizing the necessity for businesses to develop AI-specific security strategies and protocols.
Deep dives
Understanding Gen.AI Security Risks
Using generative AI tools poses significant security risks for enterprises. Employees utilizing third-party applications, such as ChatGPT or Google’s Notebook NLM, can inadvertently expose sensitive company data by allowing these tools to access and learn from internal information. Enterprises need to recognize the importance of evaluating the risks associated with these applications, particularly how they might use the data to train their models. Companies are encouraged to integrate these transformative tools into their workflows while concurrently implementing strict controls to ensure data security and compliance.
Shadow IT and Emerging AI Tools
The discussion parallels historical IT challenges, such as shadow IT, where employees adopt tools without IT oversight. The modern landscape introduces generative AI tools that can be used by employees as shortcuts to enhance productivity, often without proper organizational approval. As enterprises previously grappled with unsanctioned applications like Google Drive or Dropbox, they now face a similar situation with AI, presenting new risks that require updated risk management strategies. Organizations must recognize these emerging tools and apply established frameworks to mitigate potential security concerns associated with user-driven adoption.
Supply Chain Security in AI Development
Developing in-house generative AI applications introduces its own set of vulnerabilities linked to supply chain risks. As businesses push for faster development and deployment of AI systems, they often source components such as training data and AI models from external platforms like Hugging Face. This urgency can compromise security, as the origin and integrity of these models and datasets may remain unchecked. Organizations must scrutinize these components to ensure they do not inadvertently introduce vulnerabilities or malicious data that could lead to breaches or compliance issues.
The Evolution of Cyberattacks with AI
Cybercriminals are increasingly leveraging generative AI tools to enhance the sophistication and volume of their attacks. The effectiveness of phishing campaigns, for example, has improved significantly as attackers use AI to craft convincing messages with accurate language and formatting. The ability of hackers to utilize advanced tools allows for dynamic, novel attack methods that could bypass traditional security measures. This escalating threat landscape underscores the need for organizations to bolster their defenses by implementing AI-specific security strategies.
Framework for Securing AI Applications
To counter the risks associated with generative AI tools, enterprises are encouraged to adopt a comprehensive security framework addressing both access and runtime concerns. Access control measures should focus on monitoring and sanitizing both the data entering these applications and the outputs they generate. In terms of runtime security, organizations need to implement proactive measures that protect sensitive data from being extracted via prompt injection or other forms of exploitation. This dual approach ensures that while embracing the potential of generative AI, businesses maintain robust security postures actively safeguarding their digital environments.
AI is finding its way into more and more consumer and business applications. In particular, the widespread use of Generative AI raises a serious question: how secure is it? In this sponsored Heavy Networking episode we discuss security risks of AI tools and ways to mitigate those risks. Our guest is guest Rich Campagna, Senior... Read more »
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