Explore the pros and cons of generative AI on security. Given the need for automation and scale in security, the pros outweigh the cons.
A recent panel discussion at Black Hat 2023, Generative AI: Security Friend or Foe?, provided insights into how generative AI models like ChatGPT could impact security teams. Kelly Jackson, Editor-in-Chief of Dark Reading, moderated the roundtable with cybersecurity leaders Josh Zelonis of Palo Alto Networks, Fred Kwong of DeVry University, and analyst Curt Franklin of Omdia. The rapid emergence of generative AI presents both opportunities and risks for security professionals.
On the upside, generative models can help automate repetitive tasks and make security analysts more efficient. As Zelonis explained, AI is essential for responding at scale as environments grow more complex. Kwong highlighted benefits for threat research and education - AI can quickly summarize large amounts of data to accelerate skills development. Franklin cautioned that narrowly focused "domain expertise" AI is more realistic than general intelligence today.
Generating natural language queries and reports was cited as a key application. This could help analysts phrase questions consistently to get reliable answers. But as Franklin noted, query engineering skills will be needed to build effective AI query playbooks. Kwong also pointed to automation potential for mundane SOC tasks like gathering basic activity logs.
The panelists agreed generative AI could amplify human capabilities, but not replace security teams outright. Kwong emphasized the importance of educating employees on appropriate and ethical AI usage. Organizations will need policies and governance for AI, like any other technology capability.
On the risk side, the panel highlighted the increased sophistication of social engineering and phishing campaigns. Attackers can now generate highly convincing spear phishing content tailored to targets. Kwong noted cybercriminals are creating synthetic audio of voices cloned from public recordings to launch voice-fishing scams.
Zelonis explained that generative models provide attackers with more ammunition, but security fundamentals like zero trust remain essential. There are also potential risks of insider threats weaponizing AI or misusing access to sensitive training data. Kwong recommended security teams ask vendors about how customer data is used for AI model training.
Other challenges include the "hype vs. reality" of AI in security products. As Zelonis pointed out, organizations need to assess when it's too early to adopt versus adding strategic value. AI transparency and ethics of commercial offerings also need scrutiny.
Overall, the panel made clear that AI is here to stay, and security teams must embrace it. With strong data governance, training oversight, and a focus on human-centric use cases, generative AI can enhance threat detection and analyst productivity. But risks like voice spoofing and deep fakes require ongoing education. AI security policies need continuous adaptation. While not a magic bullet, thoughtful AI adoption can pay dividends. However, technology brings new threats that security leaders must anticipate.
Key Takeaways
Here are the key takeaways from the generative AI security discussion:
Generative AI can automate repetitive analyst tasks, making security teams more efficient and responsive.
Natural language generation for queries and reporting is a promising near-term application if implemented thoughtfully.
However, AI is not a magic solution - it augments human capabilities but doesn't replace security experts.
Strong data governance, training oversight, and policies are crucial when deploying AI tools.
Education on ethical AI use is critical, as is transparency from vendors.
Social engineering using synthesized content and voices poses an emerging generative AI threat.
Organizations should assess hype vs. reality when adopting AI security products and only integrate capabilities that add strategic value.
Fundamentals like zero trust and security training remain essential despite advances in AI.
Risks like insider threats and training data abuse require mitigation controls for AI systems.
Generative AI brings both advantages and new threats - a measured, thoughtful approach is key to maximizing benefits while minimizing risks.
In summary, the panel highlighted generative AI's promise for security but emphasized that responsible adoption, education, and smart implementation will be critical to gaining value while avoiding pitfalls.