Black Hat webinar review 2026: From alert fatigue to machine-speed defense: Using agentic AI to compress the distance between detection, investigation, and response.

 Takeaways from Blackhat webinar: Prevention at Machine Speed: Hunting Beyond Known Detections

Elastic’s approach to AI-powered security immediately stood out to me during the Black Hat webinar “Prevention at Machine Speed: Hunting Beyond Known Detections.”

James Spiteri and Paul Ewing demonstrated considerable enthusiasm when discussing Elastic’s investment in agentic AI, threat hunting, and security workflows designed to help analysts work more efficiently.

The real value of agentic AI may be its ability to reduce the amount of routine investigation that consumes an analyst’s time.

Having toured the NOC at Black Hat and security responsibilities as one of my hats, I understand how standard alerts can divert attention from more important operational and security issues. Elastic’s approach goes beyond simply having an assistant handle routine alerts. Attack Discovery can correlate related alerts, analyze security data, and provide analysts with a broader narrative of potentially malicious activity across entities such as users and hosts.

Elastic’s agentic workflows also demonstrate how AI can extend threat hunting beyond what existing detections were originally designed to find.

Elastic describes workflows that can use security data, generate queries, investigate findings, and assist analysts while keeping humans involved in important decisions. This could be particularly valuable for smaller security teams, including one-person IT or security operations teams, where reducing repetitive investigative work can provide additional capacity for production issues and higher-priority incidents.

Risk-based context is another important component in determining which security events deserve immediate attention.

Attack Discovery can group related alerts into attack narratives rather than requiring analysts to investigate every alert independently. Entity relationships and risk information provide additional context that can help analysts determine whether activity represents a genuine attack requiring further investigation.

The effectiveness of AI-driven security workflows ultimately depends on how consistently the underlying detection processes are managed.

This is where detection-as-code becomes particularly interesting. Rather than treating detection rules as static configurations, organizations can manage rules and exceptions through code-based processes, including Terraform. This approach can provide repeatability, version control, testing, and greater consistency in detection governance.

Prevention at the endpoint remains equally important because stopping malicious activity before execution can be more valuable than generating another alert afterward.

Elastic Defend combines prevention, detection, investigation, and response capabilities across Windows, macOS, and Linux. Its layered protections include behavioral protections, malware and ransomware prevention, memory-threat protection, and endpoint response capabilities.

Effective security also requires controlling who can access and modify these capabilities.

Elastic uses role-based access control (RBAC) to restrict functionality according to assigned privileges, while security policies can be configured according to organizational requirements. This becomes particularly important when different teams, users, or operational roles require different levels of security access and control.

AI-powered security introduces another security challenge: the AI itself must be protected.

Prompt injection and data-poisoning techniques represent potential risks to LLM-based systems, making guardrails, testing, and continuous security assessment important components of an AI security strategy. Elastic also maintains a bug bounty program through HackerOne, providing another avenue for security researchers to identify vulnerabilities and weaknesses.

The quality of an AI security workflow ultimately depends on the quality of the data, context, queries, and guardrails surrounding the model.

Efficient queries are particularly important because LLMs process information as tokens, meaning that unnecessarily large amounts of context can increase processing requirements, latency, and potentially cost. Focused queries and relevant context therefore become important considerations when designing scalable AI workflows.

The most important takeaway from James and Paul was that AI should augment security professionals rather than attempt to replace them.

Elastic’s approach is less about removing the human from the security process and more about compressing the time between detection, investigation, and informed action.

For organizations struggling with alert fatigue and limited security resources, that difference could be significant.

The combination of machine-speed prevention at the endpoint and AI-assisted hunting beyond known detections represents a compelling direction for security operations: allowing machines to handle more of the repetitive work while giving human analysts more time to investigate the threats that genuinely require their expertise.