Agentic AI: The 2026 threat multiplier reshaping cyberattacks

How autonomous AI agents are accelerating attacks—and what defenders must do to keep up

Key takeaways

• Unlike generative AI, agentic AI can plan, adapt, and persist autonomously, turning multi-stage attacks into continuous operations.
• Strong identity controls, network segmentation, and behavior-based detection remain effective against agentic attacks when applied consistently.
• Agentic AI doesn’t stop after a failed attempt; threat models and incident response plans must account for autonomous retry and adaptation.

There are several new threats emerging in 2026, though most are coming from groups that we’ve seen before. Ransomware groups like Qilin and Cl0p aren’t new, but they’re moving faster and using more sophisticated tactics. DireWolf and The Gentlemen were observed in 2025 but are becoming high-velocity groups with hundreds of new victims in 2026.

One of the most dangerous new threats of 2026 is not a group but a tool that adds new capabilities and enables faster attacks. Threat actors have been using generative AI (GenAI) for years to write and localize phishing content and develop malware to infect their targets. While this accelerates credential theft and initial access, it still requires human intervention to enter prompts, review output and make decisions on next steps.  

Agentic AI brings a new type of threat to the landscape. Where generative AI gives attackers better tools, agentic AI gives them a collaborative partner that can plan, act, observe, adapt, and persist without their involvement. These attacks are faster, more scalable and more dangerous to the victims.

What Is agentic AI?

Agentic AI is not a single product or tool, but an architecture of systems that can plan, decide, and execute multi‑step actions toward a specific goal. These systems typically orchestrate multiple AI agents that can adapt their behaviors in real-time. An agentic AI system typically includes the following:

• Large language model (LLM) or GenAI for reasoning and content generation
• Tools like APIs, scanners and scripts
• Memory to track what's happened across steps
• Feedback loops that allow the system to learn and adjust future actions
• Policies and rules about what it can and can't do

This combination is what enables agentic AI to conduct goal‑oriented, self‑directed attacks. You can think of generative AI as something that does excellent work when you give it the right prompt, and agentic AI as something that can carry out a project when you give it a goal. It can assemble the resources, coordinate the work and pursue the goal autonomously. Put simply, agentic AI makes multiple independent ‘attackers’ available to a single threat actor.  The agent is an operator that can conduct attacks and make decisions on the fly. Attackers no longer need a human operator to adjust malware or tactics when an attack is blocked. Agentic AI can respond and adapt while it is in the system, and it will continue trying until it finishes the operation or is shut down. Tasks that previously required an experienced threat actor to plan, coordinate, and execute over days or weeks can now be delegated to an agent that runs continuously until it achieves its goal or gets shut down.

These attacks are just beginning to emerge, and they are certain to accelerate throughout the year. Red teams and security researchers are already demonstrating agentic attack capabilities in controlled environments, and criminal groups are moving quickly to deploy. We have already seen one clear example of an agentic-style AI attack when a threat actor targeted FortiGate firewalls to gain access and conduct reconnaissance on victim networks. You can get a breakdown of the attack here.

Defending against agentic AI attacks

The good news is that the defenses that matter most against agentic attacks are already accessible. Strong identity controls, network segmentation, behavior-based detection, and rapid, well-practiced incident response can prevent these attacks or minimize the damage. The technology gap between defenders and attackers is real but not insurmountable.

What's required is a shift in how defenders think about the threat. Threat models should be based on how well the defenses hold up against an autonomous attack agent that may be faster than you’ve ever seen before. Once the attack is in your system, can your defenses hold up against the intelligence, adaptability and persistence of the agent? Keep in mind that attack reconnaissance happens continuously and automatically, not just in a defined pre-attack phase. Furthermore, blocked attacks will resume automatically once the agent adapts to the block. The agent must be purged completely to be contained.

You also need to include behavior- and anomaly-based monitoring in your system. Look for unusual access to management tools, automation platforms, or service accounts doing things outside their normal pattern. 

Agentic AI represents a fundamental shift in how cyberattacks are planned and executed, but it doesn’t make defense impossible. Organizations that invest in strong identity controls, behavior-based detection and rapid incident response will be best positioned to disrupt autonomous attacks before they can complete their objectives. If you haven’t yet adapted your defenses for agentic AI attacks, you should get started now.