September 11, 2024
Sponsored by Leidos
Leveraging AI for Mission Success: Use Cases, Adoption, and Cybersecurity Challenge
Seth Abrams, Chief Technology Officer for Homeland and Force Protection, and Carolyn Chipman, Vice President for Homeland and Force Protection Growth at Leidos, discuss the increasing role of AI in government agencies and the critical importance of using mission-specific use cases to demonstrate AI’s value. Abrams explains that use cases allow for more practical and focused conversations with agencies, helping to clarify how AI can address specific mission needs. Chipman emphasizes that for AI adoption to succeed, it’s essential to ensure safety, conduct small-group testing, and build user trust, which accelerates broader adoption. They also touch on AI-powered chatbots, which are streamlining internal and external processes by reducing paperwork and enhancing efficiency, as demonstrated by Chipman's own experience with a chatbot resolving a technical issue.
Abrams and Chipman further explore AI’s role in cybersecurity, describing it as a "cat-and-mouse" game where both adversaries and defenders are leveraging AI for offense and defense. Abrams highlights how AI can amplify the capabilities of human analysts, but also raises concerns about the risks posed by adversaries using similar technologies. Chipman emphasizes the importance of maintaining a "threat mindset" when deploying AI, urging agencies to carefully vet the provenance of AI products to avoid vulnerabilities. Both experts agree that managing data security and understanding where data is stored and used in AI models are critical, especially as agencies work to balance the opportunities and risks AI presents.
Key Takeaways:
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AI adoption in federal agencies is most effective when tied to specific use cases, as it helps clarify mission needs and foster meaningful discussions with stakeholders.
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While AI can enhance cybersecurity defenses, it also creates new risks, requiring a proactive approach to stay ahead of potential adversaries.
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Ensuring the safety, reliability, and ethical sourcing of AI technologies before deployment, to safeguard data and maintain public trust is pivotal
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