06-08, 14:45–15:30 (Europe/London), Doddington Forum
This talk will detail how to integrate external threat intelligence data into an autonomous agentic AI system for proactive cybersecurity. Using real world datasets—including open-source threat feeds, security logs, or OSINT—you will learn how to build a data ingestion pipeline, train models with Python, and deploy agents that autonomously detect and mitigate cyber threats. This case study will provide practical insights into data preprocessing, feature engineering, and the challenges of adversarial conditions.
In an era where cyber threats are growing both in complexity and frequency, harnessing external threat intelligence can provide a decisive edge in cybersecurity. This session offers a deep dive into developing autonomous agentic AI systems that leverage publicly available threat data to drive proactive defense mechanisms.
Key Focus Areas:
Integrating External Data: Learn strategies to ingest, clean, and harmonize diverse external datasets—such as open-source threat feeds, OSINT, and incident logs—with your internal security data, creating a comprehensive situational awareness.
Agentic AI in Cyber Defense:
Understand the core principles behind agentic AI and its application in autonomous cybersecurity systems. Discover how AI agents can continuously monitor network behavior, learn from evolving threats, and execute proactive countermeasures.
Addressing Security Challenges:
Delve into the challenges of deploying autonomous systems in adversarial environments. The talk will cover best practices for mitigating vulnerabilities, including strategies to combat adversarial attacks and data poisoning.
No previous knowledge expected
Jyoti is an Applied Cyber Security Data Scientist at Microsoft, UK. Jyoti has a total of 8 years of experience in top notch technologies like blockchain, cybersecurity and finance industry. Jyoti has primarily worked on the LLM, fine-tuning and agent AI systems.