Key Takeaways:

• The attack window has collapsed to 22 seconds. The time between initial access and threat handoff dropped from 8 hours in 2022 — making traditional security workflows effectively post-mortems.

• Adversaries are already running mature agentic AI operations. Attackers use AI agents to automate ransomware data analysis, reconnaissance, and evasion while defenders are still evaluating adoption.

• Manual intelligence routing is a critical bottleneck. When CTI and incident response teams are disconnected, speed is lost exactly where it matters most.

• Collective defense requires automated sharing infrastructure. Legal friction and format incompatibility block meaningful intelligence sharing — technical architecture has to solve what policy cannot.

• AI agents are the new attack surface. With 1.3 billion agents projected by 2028, security teams must now track prompt injection, model poisoning, shadow agents, and unauthorized model access.

• The gap isn't detection, it's operationalization speed. Better detection means nothing if threat data can't be connected to defensive action in seconds.

Introduction

The RSAC 2026 theme, "The Power of Community," arrived at a critical moment for the industry. While much of the discussion focused on the rise of autonomous machines, the underlying message was clear: no organization can withstand machine-speed attacks in isolation. True resilience now depends on a community's ability to pool intelligence and coordinate responses at the same pace as the adversaries. 

When Did Passive Defense Become a Liability? 

Sandra Joyce (VP, Google Threat Intelligence) opened the conference with a staggering figure: the window between initial access and threat handoff has collapsed from 8 hours in 2022 to just 22 seconds. 

At this speed, traditional security processes function as post-mortems. To achieve actual security outcomes, intelligence must be connected to defensive actions within seconds. If threat data is not integrated into active workflows immediately, it becomes historical data rather than a functional defense.

Is Agentic AI Already Scaling Attacker Operations?

The asymmetry between attackers and defenders was a central theme. While many CISOs are still evaluating AI adoption, adversaries have already matured their agentic AI operations.  

At RSAC, Trend Micro researchers demonstrated "VibeCrime," showcasing how agentic systems now automate ransomware data analysis. In these attacks, AI agents rapidly parse through terabytes of exfiltrated data to identify the most sensitive files for extortion. 

These AI agents manage reconnaissance, extortion, and evasion while human adversaries provide high-level direction. This creates a technical asymmetry where attackers run mature, automated operations while many defenders are still in the early stages of AI evaluation.

Why Does Threat Intelligence Struggle with the Handoff Gap?

A session featuring Target highlighted a persistent problem in Cyber Threat Intelligence (CTI): the friction between analysts who produce intelligence and the incident response teams who act on it.

In many organizations, intelligence is still routed through static reports or manual tickets. This creates a bottleneck at the point where speed is most critical. While adversaries have automated their processes, many defenders rely on manual data routing.

Unified threat intelligence management addresses this by bridging the divide between CTI and response. The Cyware Intelligence Suite automates this process by normalizing data from over 400 integrations and connecting it directly to existing security tools. This allows analysts to spend less time routing data and more time responding to threats.

What Does Effective Collective Defense Require?

The "Power of Community" theme at RSAC 2026 focused on the barriers to intelligence sharing. Leaders from various Information Sharing and Analysis Centers (ISACs) identified legal friction and format incompatibility as the primary obstacles.

Meaningful collective defense requires a technical architecture that supports automated sharing. The UAE’s "Crystal Ball" initiative is one example of using AI agents to exchange threat data across borders automatically. Cyware Collaborate facilitates this by providing the communication and collaboration layer for over 85% of major ISACs. It allows teams to co-author advisories and coordinate crisis responses in real-time, ensuring that a threat identified by one member becomes a defense for the entire community.

How Do AI Agents Change the Attack Surface? 

Microsoft's Vasu Jakkal projected that 1.3 billion AI agents will be in operation by 2028. This shifts the attack surface from static endpoints to dynamic, machine-generated entities.

As agentic AI becomes integrated into business operations, threat intelligence must expand to include attacks directed at the AI models themselves. Discussions at RSAC highlighted a rise in adversarial machine learning techniques, such as prompt injection and model poisoning, where attackers attempt to manipulate an AI’s logic or exfiltrate its training data.

When AI agents become the attack surface, security teams need intelligence on "AI Security" threats. This includes tracking "shadow agents" operating without oversight and monitoring for unauthorized access to model weights or API keys. Intelligence must be structured and continuously updated to detect these specialized threats, ensuring that the AI tools meant to help defenders do not become a liability.

Conclusion 

RSAC 2026 confirmed that the primary challenge for security teams is not detection capability, but the speed of action. Improving detection is insufficient if the infrastructure for sharing and operationalization remains slow.

The Cyware Threat Intelligence Platform is built for exactly this moment: unifying threat intelligence management, enabling collective defense at scale, and delivering the AI-powered workflows teams need to turn threat data into threat defense. 

FAQs 

How can threat intelligence teams measure if their data is being operationalized in time? 

Teams should focus on Mean Time to Action (MTTA). This measures the time from the moment an indicator is ingested to its deployment as a block-rule in a firewall or EDR. A successful program aims for an MTTA that matches the seconds-long window used by modern attackers.

What infrastructure is needed before using agentic AI in threat intelligence workflows?

The primary requirement is a unified data foundation. AI agents require clean, normalized data from multiple sources to function accurately. Organizations should implement a platform that can de-duplicate and correlate feeds before layering on AI agents for orchestration.

What are the biggest structural barriers to effective threat intelligence sharing? 

Technical interoperability and legal concerns regarding data privacy are the main barriers. Using standardized formats like STIX/TAXII and implementing automated TLP (Traffic Light Protocol) tagging helps organizations share relevant data without exposing sensitive internal information.

How should security teams rethink CTI programs as AI agents become more common? 

CTI programs must shift from focusing on static indicators to analyzing behavioral patterns (TTPs). Because AI agents can generate unique indicators at scale, identifying the underlying behavior is the only way to maintain a proactive defense.