Today's adversaries run on agentic AI - autonomous, relentless, and operating at machine speed. Defenders, largely, do not.

The Attacker’s New Advantage

“Beyond empowering novice offenders, AI stands to intensify the scale and sophistication of attacks by seasoned cybercriminals,” notes a recent research paper examining the evolving relationship between cybercriminals and AI. Attacks are now being industrialized and fully automated across every stage of the kill chain: 

• Mass phishing campaigns, tailored to context, developed in seconds 

• Automated identification of exposed services and chained vulnerabilities 

• Continuous mutation of payloads to stay under the radar  

• Reprogramming and rewriting malware strains according to defense response 

A joint report from the Cloud Security Alliance (CSA), the SANS Institute, and the Open Worldwide Application Security Project (OWASP) concludes that in the near term, organizations are “likely to be overwhelmed” by threat actors using AI to find and exploit vulnerabilities faster than defenders can patch them.

Meanwhile, defenders are still running the same playbook: SIEMs, SOARs, automation tools, and analysts stitching it all together by hand. 

The asymmetry is stark. While cybercrime has gone all but completely agentic, cybersecurity is still playing catch-up.  

This point was explored in depth at RSAC last month, with participants underscoring the fact that agentic AI didn’t just upgrade adversaries; it exposed a structural fault line in how defenders operate.

Why Traditional TI Breaks Here

Traditional threat intelligence was built for a different era — one where human analysts chased threats operated by human counterparts. When threats were human-paced, defenders stood a fighting chance.  

But things are different now. Attackers now have a structural advantage: agentic systems continuously perform reconnaissance, exploitation, and evasion without waiting for human intervention or feedback cycles. 

Defenders, meanwhile, remain trapped in a fragmented process: analysts manually triage alert floods across SIEMs, EDRs, and threat feeds, stitch together static indicators, and perform contextual enrichment before any decision can be made. By the time intelligence reaches usable form, the attack has already moved on. 

The friction now becomes an opportunity missed against an opponent who never stops. 

• Switching tool costs become compound frictions that add up with each investigation 

• Outdated IOCs never even get to the analyst’s desk, shutting down the time frame for action 

• Context comes too late, as attackers continue their operations, out-evolving our efforts to catch them 

• The lag between reporting and action means intelligence generation loses its connection to action 

The Fix: Agentic TI That Matches Agentic Threats

The answer is matching adversary capability with defender capability, an AI-powered infrastructure that can plan, execute, and adapt at the same speed. SOCs need AI solutions that can handle their complex, multi-step processes at scale: planning, executing, validating, and adapting to emerging threats without needing to ask for permission at each step. 

More intelligence isn’t what’s needed. Rather, it’s the ability to operationalize it in real time without relying on human intervention. Cyware AI makes this happen. With Cyware, purpose-built AI agents are embedded right where analysts naturally work: directly in the browser (Chrome extension) and natively inside Cyware Intel Exchange via the Agent Hub. This means agents can surface on demand when needed, without interfering with existing workloads. Analysts stay in control of strategy and decisions; agents handle the multi-step execution in between. 

At the same time, Cyware Orchestrate silently powers agentic workflows in the background, letting analysts use natural language queries to deep dive and eliminating the friction of playbooks and pipelines. 

Cyware AI deploys a suite of specialized agents, each trained for a distinct SOC function:  

• Threat Intelligence Agent: This agent summarizes feeds, enriches IOCs, profiles threat actors, and determines if signals are relevant to the environment. The result is a 50-70% acceleration in CTI workflow (without sacrificing depth).  

• SOC Analysis Agent: This agent enriches IOCs, connects signals across tools (SIEM, EDR, ticketing), and prompts mitigation steps, all from the same tool. No more overhead swivel-chair burden, and 2-3x triage reduction in the process. 

• Detection Engineering Agent: This agent converts threat context (IOCs, TTPs) into ready-to-use detection rules and validated Splunk SPL queries automatically. No more hand-tuning signals and falling behind new threats. 

• Attack Flow Agent: This agent transforms static indicators into adversarial behaviors mapped against MITRE ATT&CK. Now SOCs are on the offensive, proactive side of things and able to predict attackers’ next moves. 

Crucially, every agent action must be logged, auditable, and fully traceable. 

Every action taken by Cyware agents is tracked, traced, and recorded in full detail, providing comprehensive oversight of the entire decision-making and execution process for intelligence. 

At the same time, Cyware enables intelligence sharing between ISACs and ISAOs through STIX/TAXII, while retaining control over the sharing process. This shifts the collaboration process from an informal exchange into a governed, rapid, intelligence network. 

Fighting Agentic with Agentic 

Agentic AI broke threat intelligence by making the attacker's side autonomous while leaving the defender's side manual. The fix follows the same logic: you don't beat autonomous attacks with faster humans. You beat them with better agents. 

That's not a prediction. For SOCs still operating on legacy TI infrastructure, it's already a gap. See how Cyware AI closes it. 

Frequently Asked Questions (People Also Ask)

1. How is agentic AI in threat intelligence different from the AI features already built into my SIEM or TIP?

Traditional SIEM and TIP features focus on descriptive AI that summarizes data or executes rigid, linear playbooks when prompted by a human. Agentic AI is fundamentally different because it is goal-oriented and autonomous, meaning it can plan and execute complex, multi-step security workflows across different tools without needing constant human intervention at every stage

2. If attackers are using agentic AI to exploit LLM weaknesses, doesn’t adding AI agents to my security stack introduce new risk?

Integrating AI agents does introduce a new attack surface, but this risk is managed through human-in-the-loop architectures and strict permissioning. Unlike the open-ended AI used by attackers, defensive agents operate within a controlled environment where every action is logged and auditable. This ensures that while the AI operates at machine speed, it remains confined to authorized security protocols and cannot deviate from established safety guardrails.

3. How do Cyware AI agents support threat intelligence sharing and collaboration across ISACs and partner organizations?

Cyware AI agents automate the labor-intensive process of sanitizing, normalizing, and distributing intelligence via STIX/TAXII standards to various ISACs and ISAOs. By identifying and sharing relevant threat indicators in real-time, these agents transform slow manual exchanges into a proactive, governed network that protects entire industry sectors simultaneously.