Fight AI with AI: Going Beyond ChatGPT

Don’t miss out on an insightful talk that explores the future of cybersecurity in the era of generative AI. Join us for a dynamic panel discussion featuring industry experts:

  • Brian Black: Director of Sales Engineering
  • Scott Chennells: Distinguished Engineer
  • Mark Vaitzman: Threat Lab Team Leader

In this session, we’ll delve into the exciting world of generative AI and its impact on cybersecurity. As attacks become more sophisticated and adaptable, it’s crucial to understand the strategies employed by attackers using generative AI tools like AutoGPT and DarkBERT. Gain valuable insights on:

  • The role of generative AI in an attacker’s arsenal
  • How generative AI is challenging existing cybersecurity solutions
  • Key concerns surrounding the abuse of generative AI
  • The effectiveness of deep learning-based AI in combating AI-generated threats

Future-proof your cybersecurity stack by attending this informative session on Wednesday, July 12th at 11 AM ET. Register now to ensure you’re equipped to navigate the evolving landscape of cybersecurity in the face of generative AI.

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Aihub Team

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AI and Data Privacy: Balancing AI advancements with privacy concerns and techniques for privacy-preserving AI.

AI and Virtual Assistants: AI-driven virtual assistants, chatbots, and voice assistants for personalized user interactions.

AI and Business Process Automation: AI-powered automation of repetitive tasks and decision-making in business processes.

AI and Social Media: AI algorithms for content recommendation, sentiment analysis, and social network analysis.

AI for Environmental Monitoring: AI applications in monitoring and protecting the environment, including wildlife tracking and climate modeling.

AI in Cybersecurity: AI systems for threat detection, anomaly detection, and intelligent security analysis.

AI in Gaming: The use of AI techniques in game development, character behavior, and procedural content generation.

AI in Autonomous Vehicles: AI technologies powering self-driving cars and intelligent transportation systems.

AI Ethics: Ethical considerations and guidelines for the responsible development and use of AI systems.

AI in Education: AI-based systems for personalized learning, adaptive assessments, and intelligent tutoring.

AI in Finance: The use of AI algorithms for fraud detection, risk assessment, trading, and portfolio management in the financial sector.

AI in Healthcare: Applications of AI in medical diagnosis, drug discovery, patient monitoring, and personalized medicine.

Robotics: The integration of AI and robotics, enabling machines to perform physical tasks autonomously.

Explainable AI: Techniques and methods for making AI systems more transparent and interpretable

Reinforcement Learning: AI agents that learn through trial and error by interacting with an environment

Computer Vision: AI systems capable of interpreting and understanding visual data.

Natural Language Processing: AI techniques for understanding and processing human language.