AI is changing not just how work gets done, but how cyberattacks are conceived and executed. Darktrace’s 2026 outlook underscores a pivotal shift. Attackers are increasingly targeting the intelligence layer of enterprises. Darktrace cautions in its 2026 forecast that AI is altering more than simply how companies run. It is radically altering the way that attackers think, grow, and take advantage of trust.
This evolution marks the beginning of a new era of cybersecurity, one where trust itself becomes the primary attack surface.
Trend 1: Prompt Injection Moves from Theory to Front-Page Breach
“We’ll see the first major story of an indirect prompt injection attack against companies adopting AI, either through an accessible chatbot or an agentic system ingesting a hidden prompt. In practice, this may result in unauthorized data exposure or unintended malicious behavior by AI systems, such as over-sharing information, misrouting communications, or acting outside their intended scope. Recent attention on this risk—particularly in the context of AI-powered browsers and additional safety layers being introduced to guide agent behavior—highlights a growing industry awareness of the challenge.”
According to Collin Chapleau, Senior Director of Security and AI Strategy, this marks a turning point in enterprise AI security awareness.
Trend 2: SaaS Platforms Become the Preferred Supply Chain Target
“Attackers have learned a simple lesson: compromising SaaS platforms can have big payouts. As a result, we’ll see more targeting of commercial off-the-shelf SaaS providers, which are often highly trusted and deeply integrated into business environments. Some of these attacks may involve software with unfamiliar brand names, but their downstream impact will be significant. In 2026, expect more breaches where attackers leverage valid credentials, APIs, or misconfigurations to bypass traditional defenses entirely.”
– Nathaniel Jones, VP of Security & AI Strategy
Trend 3: AI removes the attacker bottleneck—smaller organizations feel the impact
“One factor that is currently preventing more companies from breaches is a bottleneck on the attacker side: there’s not enough human hacker capital. The number of human hands on a keyboard is a rate-determining factor in the threat landscape. Further advancements of AI and automation will continue to open that bottleneck. We are already seeing that. The ostrich approach of hoping that one’s own company is too obscure to be noticed by attackers will no longer work as attacker capacity increases.”
–Max Heinemeyer, Global Field CISO
Humans are even more outpaced, but not broken:
“When it comes to cyber, people aren’t failing; the system is moving faster than they can. Attackers exploit the gap between human judgment and machine-speed operations. The rise of deepfakes and emotion-driven scams that we’ve seen in the last few years reduce our ability to spot the familiar human cues we’ve been taught to look out for. Fraud now spans social platforms, encrypted chat, and instant payments in minutes. Expecting humans to be the last line of defense is unrealistic. Defense must assume human fallibility and design accordingly. Automated provenance checks, cryptographic signatures, and dual-channel verification should precede human judgment. Training still matters, but it cannot close the gap alone. In the year ahead, we need to see more of a focus on partnership: systems that absorb risk so humans make decisions in context, not under pressure.”
Margaret Cunningham, VP of Security & AI Strategy
From Reactive Defense to AI-Aware Security Architecture
Taken together, these emerging threats point to a deeper shift. Cybersecurity can no longer operate as a reactive layer applied after AI systems are deployed. In 2026, security must be designed alongside AI, not bolted on after the fact.
Traditional controls were built to protect static applications, predictable user behavior, and well-defined perimeters. AI systems break all three assumptions. They learn continuously, ingest external context, and make probabilistic decisions that evolve. This creates a new class of risk. One that sits at the intersection of data integrity, system autonomy, and trust.
For enterprises, this means security teams will need tighter collaboration with AI, product, and platform leaders. Threat modeling must account for how models reason, not just how software executes. Governance must extend beyond access control to include decision boundaries, escalation logic, and behavioral drift.
What This Means for 2026 and Beyond
Darktrace’s 2026 outlook paints a clear picture. AI is compressing timelines, expanding attack surfaces, and shifting trust boundaries faster than legacy security models were built to handle.
The organizations that succeed will not be those that simply deploy more AI. They will be the ones who redesign security architectures for autonomy, assume compromise, and treat AI behavior as a first-class risk domain.
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