The cyber threat landscape is evolving at an accelerated pace each year, thanks to attackers’ expanding creativity and advances in technology.
The same AI breakthroughs that promise efficiency and automation for businesses are being weaponized by adversaries, creating a perfect storm of risk. From attacker-oriented LLMs to hyper-personalized social engineering, the pace of innovation from threat actors in 2026 will challenge even the most mature security programs.
The researchers and forensics experts behind Varonis Threat Labs study these shifts in cybercrime daily, dissecting attack patterns, vulnerabilities, and loopholes to anticipate what’s next.
We asked the team what they see brewing in the world of cybercrime in 2026, and where organizations need to rethink security. Here’s what they said:
Hyper-personalized social engineering (at scale)
With the rise of Internet-accessible LLM-based research, common consumer models are no longer restricted to potentially outdated training data and can now assist users in directly accessing and researching topics through the Internet. This relatively-new capability is a massive boon to the speed at which phishing lures can be crafted – across languages, business verticals, and otherwise, there is no longer any meaningful knowledge limitation to threat actors when creating social engineering payloads.
Even post-compromise, once a threat actor has access to a valid mailbox, they may utilize LLMs such as Copilot to better understand the context of foreign-language email chains to ensure a maximized success chance of subsequent social engineering tactics.
Forget typos and awkward phrasing — phishing has entered a new era. In fact, according to our State of Phishing report, there has been a 703% increase in credential phishing in the last year.
AI-powered phishing emails are near flawless, contextually accurate, and eerily personal. They mimic colleagues, brands, and even your own writing style. Threats use deepfakes, vishing, and more to confuse you even more. The old “trust what looks right” mindset is gone.
To stay safe, think verification, not speed. If an email asks for credentials, money, or urgent action, confirm the request through a separate channel such as a call, text, or using the official app. Type web addresses yourself or use saved bookmarks versus clicking links willingly. Be cautious with attachments, even if they look routine.
Multifactor authentication (MFA) is also critical because it provides an added layer of security if there is a slip up. At the end of the day, remember that if something feels off, it probably is. AI makes phishing convincing, but it can’t beat human skepticism.
Deepfake evolution
While not new, the concept of ‘deep faked’ audio or video has typically been a niche capability requiring highly specific source material or computation. Now, recreating video and audio, simultaneously, with extraordinarily little source data to draw from, is more common than ever.
This tactic will enhance the success likelihood of common attack vectors such as CEO impersonations, fraud, and other social engineering scams such as help-desk call-ins, external Teams/Zoom calls, and more.
Companies must begin to implement additional identity verification checks for users on their front-lines such as help desks or call centers. Without such checks, it is only a matter of time before a cyber compromise or massive fraud event occurs within your organization.
Over-privileged chatbots will ignite data breaches
AI copilots promise efficiency, but they also introduce dangerous blind spots. When chatbots or similar enterprise-focused LLMs are granted excessive permissions, a single compromised identity (and potentially an unauthenticated user) can lead to disaster.
As enterprises rush to adopt Generative AI across their data landscapes, the plan for securing and auditing these models is not being given enough attention. This is especially true when connecting models to multiple Model Context Protocol (MCP) servers that can execute actions on behalf of the LLM. Many organizations are integrating LLMs into their data workflows, including access to Exchange Online mailboxes, knowledge stores such as Confluence, code repositories, file-shares, databases, and many other data sources. If access to these models is not properly secured or is exposed externally without appropriate protections, finding and exfiltrating sensitive data will become trivial for attackers, who need only provide effective prompts.
Our team has demonstrated this risk in controlled environments countless times: one compromised account can lead to the discovery of thousands of overexposed files, including financial records and intellectual property – and this is before LLMs are. It is only a matter of time before this plays out in the real world, and the result will be breaches that cost millions and erode trust.
Rise in abuse of LLMs and agentic AI
In addition to off-the-shelf tools such as Claude or ChatGPT, advanced attackers are almost certainly taking advantage of open-source models to advance their objectives.
By stripping away or weakening the ethical guardrails in open-source AI tools, self-hosted LLMs enable attackers to automate tasks where AI excels - such as large-scale information gathering, rapid data summarization, and adaptive retries of enumerations and exploits. This systematic approach dramatically accelerates attack chains, integrates offensive tooling, and lowers the barrier for sophisticated breaches, even for low-skill actors.
LLMs also excel at code generation – and models trained to create ‘offensive’ focused software will be just as capable as any other when trained on the large quantities of open-source red team tools available on sites such as GitHub.
In short, the rise of generative AI tooling is massively increasing the efficiency of threat actors. Whether through augmentation of recon or development capabilities, enhancing their phishing effectiveness, or assisting in data analysis, there is no doubt that just as much as it helps defenders, it is also helping attackers.
How to combat cybercrime in 2026
Attackers evolve just as defenders do – and we must strive to keep pace with new techniques to ensure effective hardening of enterprise networks. Controlling, auditing, and monitoring enterprise AI models and connected MCPs will be key in the coming years to ensuring that they do not lead to a breach of your network.
Cybercrime isn’t slowing down — and neither are we. The threats we’ve outlined aren’t distant possibilities; they’re already taking shape. Attackers are innovating faster than traditional defenses can keep up, and the cost of inaction will be measured in millions.
The best way to outsmart threats is with a data-centric security strategy. By focusing on what threats are after, it’s easier to keep them away from what matters most: your data.
Varonis Threat Labs is on the frontlines, uncovering new attack techniques, analyzing real-world breaches, and developing strategies to keep organizations ahead of adversaries. In 2026, we’ll continue to share cutting-edge research, actionable insights, and proven methods to help you find, fix, and alert threats before they become breaches.
Follow us throughout the year for expert guidance and practical steps to secure your data in an AI-driven world. Because the future of cybersecurity isn’t about reacting — it’s about anticipating what’s next.
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