Conference Description
Key Takeaways
- Academic workshop exploring how Large Language Models and AI can transform cyber threat intelligence workflows
- Addresses critical challenges in processing high-volume, rapidly evolving threat data at scale
- Covers emerging applications in IoT security, 6G networks, and federated learning for threat detection
- Designed for cybersecurity researchers, threat intelligence analysts, and AI/ML engineers working in security
- Co-located with the Annual Computer Security Applications Conference (ACSAC) 2026 in Los Angeles
Introduction
The Workshop on AI for Cyber Threat Intelligence (WAITI) 2026 brings together researchers, security practitioners, and AI specialists to examine how artificial intelligence—particularly Large Language Models and natural language processing—can reshape the way organisations collect, analyse, and act upon threat intelligence. As adversarial tactics grow more sophisticated and the volume of security telemetry continues to outpace human analysis capacity, the cybersecurity community faces mounting pressure to adopt automated, scalable approaches. WAITI 2026 provides a technical forum for presenting research advances and discussing the practical challenges of deploying AI-driven intelligence systems in production environments.
About This Event
WAITI 2026 is an in-person, research-focused workshop co-located with the Annual Computer Security Applications Conference (ACSAC) 2026 in Los Angeles, California. The event operates under the IEEE Computer Society as its publication partner, positioning accepted papers within a recognised academic framework. The workshop format emphasises technical depth through paper presentations, hands-on sessions, and collaborative discussions designed to foster connections between academic researchers and industry practitioners.
Unlike vendor-driven conferences, WAITI maintains an educational orientation centred on advancing the field rather than commercial promotion. This structure encourages candid discussion of both the capabilities and limitations of current AI approaches to threat intelligence.
Core Technical Themes
The workshop programme spans several interconnected areas where AI techniques intersect with cyber threat intelligence requirements. A central focus is the application of Large Language Models to automate intelligence extraction from unstructured sources—threat reports, dark web forums, vulnerability disclosures, and incident documentation that traditionally require extensive manual review.
Deep learning approaches to threat detection and malware analysis form another significant thread, examining how neural architectures can identify patterns across network traffic, endpoint behaviour, and code samples. Related sessions address threat hunting methodologies, attribution challenges, and prioritisation frameworks that help security teams focus limited resources on the most consequential risks.
The programme also explores IoT security and network intrusion detection, areas where the proliferation of connected devices has dramatically expanded attack surfaces. Emerging topics include AI applications in 6G network environments, federated learning approaches that enable collaborative threat detection without centralising sensitive data, and agentic AI systems capable of autonomous security operations.
Responsible AI in Security Operations
WAITI 2026 dedicates attention to the governance dimensions of AI-driven security systems. Sessions on explainable AI address a persistent challenge: security analysts need to understand why an automated system flagged particular activity as malicious, both for operational confidence and regulatory compliance. Bias mitigation in threat detection models presents another area of active research, as training data imbalances can lead to blind spots or disproportionate false positive rates across different network environments.
Ethical and legal considerations receive dedicated coverage, reflecting growing regulatory scrutiny of automated decision-making in security contexts. These discussions acknowledge that deploying AI for threat intelligence involves trade-offs between detection capability, privacy implications, and accountability requirements.
Who Should Attend
The workshop serves professionals working at the intersection of artificial intelligence and cybersecurity. Security researchers and academics investigating novel detection methods will find opportunities to present findings and receive peer feedback. Threat intelligence analysts seeking to understand how LLMs might augment their workflows can evaluate current research against operational requirements.
AI and machine learning engineers building security applications benefit from exposure to domain-specific challenges that distinguish threat detection from other classification problems. Network security engineers, security architects, and technical leaders responsible for defensive infrastructure gain insight into emerging capabilities that may influence future tooling decisions. Policy makers and legal experts examining the regulatory landscape for AI in cybersecurity will find relevant discussions on accountability and governance frameworks.
The Shift Toward Proactive Defence
A recurring theme throughout WAITI 2026 is the transition from reactive incident response toward anticipatory security postures. Traditional threat intelligence workflows often struggle to keep pace with adversaries who continuously adapt their techniques. By automating the extraction and correlation of threat indicators, AI systems can potentially compress the time between threat emergence and defensive response—though realising this potential requires addressing significant challenges in accuracy, context preservation, and integration with existing security operations.
The workshop provides a venue for examining these challenges honestly, sharing both successful approaches and instructive failures in applying AI to real-world threat intelligence problems.