Despite fears that AI overwhelmingly favours attackers, defenders are entering 2026 stronger than expected—especially if they adapt fast enough.
Generative and agentic AI gives threat actors the ability to scale faster, become more convincing, and bypass defences with ease. Yet despite rapid AI adoption on both sides, the breaches dominating headlines today remain only minimally AI-aided.
While financial loss and operational disruption from attacks remain high, they haven’t spiked in proportion to the fear surrounding large language models. This gap between expectation and reality indicates that defenders aren’t falling behind—in fact, they are ahead of where many expected them to be.
Looking at the year ahead, this advantage is likely to become clearer. Not because attackers will suddenly lose momentum, but because defenders are learning how to operationalise AI across detection, analysis, and response in ways that compound over time.
Attackers’ AI advantage has been overstated
Much of the anxiety around AI in cybersecurity assumes that access to advanced models automatically translates into a decisive edge for attackers.
However, we have yet to see any dramatic leaps in capability, and the ways threat actors are using AI today remain largely incremental. Generative tools help write scripts faster, polish phishing copy, or accelerate reconnaissance, but they have not fundamentally changed how most attacks succeed.
What is striking is how little of today’s most damaging activity can be clearly attributed to AI-native techniques. Even where attackers rely on large language models to assist with coding, those contributions tend to sit at the margins of an attack chain rather than at its core. The feared surge of AI-driven breaches remains largely hypothetical.
By contrast, the cybersecurity industry has arguably captured outsized value from AI so far. This is largely because defenders operate in a highly structured environment, with access to years of historical data, organisation-wide visibility, and continuous feedback loops that allow AI systems to improve over time.
Security teams have long moved beyond treating AI as a standalone tool and have integrated it into detection pipelines, alert triage, prioritisation, and automation. As these systems learn from historical telemetry and organisational context, they become more accurate and more resilient in ways attackers cannot easily replicate.
Why the defenders’ advantage may not last
While we might be ahead today, the underlying threat of AI attacks has only been deferred, not defeated.
Current AI-powered threats are only the tip of the iceberg, and the real dangers have yet to emerge. When they do arrive, they are unlikely to resemble the automated phishing campaigns security teams have spent years optimising against.
The real risk lies in hyper-personalised social engineering. Instead of relying on generic templates or volume, AI enables attackers to tailor messages to individuals with unsettling precision. Public data, social graphs, communication history, tone, timing, and relationships can all be synthesised into messages that are indistinguishable from legitimate interactions. These attacks don’t rely on exploiting technical weaknesses, but by collapsing the boundary between trusted and untrusted communication.
What makes this shift especially dangerous is that it doesn’t rely on advancing tech and is already possible today. What has been missing is scale and operational maturity among attackers. That gap will not persist indefinitely, and when personalised attacks move from isolated experiments to systematic campaigns, many existing assumptions will fail at once.
From content inspection to behavioural detection
As generative AI improves, one of its most immediate effects has been the erosion of traditional social engineering signals. Poor grammar, awkward phrasing, and suspicious formatting are no longer reliable indicators of malicious intent. AI-generated communication is increasingly clean, fluent, and contextually appropriate, rendering content inspection a rapidly diminishing signal for threat detection.
This forces a structural shift in how security systems must operate. When the message itself can no longer be trusted as a differentiator, the most durable signals that remain are behavioural. How often does someone receive this type of request? Is the action consistent with historical communication patterns? Does the timing, frequency, or relationship context deviate from established norms? These are dimensions that AI cannot readily manipulate because they are grounded in history and human understanding, not presentation.
The good news is that, while these threats are becoming more prevalent, powerful behavioural anomaly detection is also no longer an advanced capability reserved for the most mature organisations. It is becoming the baseline requirement for maintaining visibility in an environment where content is increasingly indistinguishable from legitimate communication.
AI agents and the collapse of legacy trust models
One of the clearest signs that today’s advantage is conditional can be seen in how organisations are adopting autonomous AI agents. These systems behave less like conventional software and more like trusted insiders, capable of taking actions, accessing data, and executing tasks with minimal human oversight. Yet many security models still evaluate them through a traditional software lens.
The problem is not malicious intent, but misplaced trust. Autonomous agents inherit the privileges of the humans they assist, while audits focus on vendor assurances rather than on agent behaviour or user-supplied inputs. This creates blind spots where normal activity becomes an unmonitored attack vector.
A high-profile incident involving AI-driven tooling would not demonstrate that AI is inherently unsafe. It would reveal that security frameworks built for deterministic software do not translate cleanly to probabilistic, autonomous systems. Maintaining the upper hand requires shifting from static approval toward continuous verification of actions, access patterns, and outcomes.
Winning 2026 depends on what defenders do next
The AI cyber battle is not tilting toward attackers in the way many once feared. Defenders are entering 2026 in a position of strength because they have learned how to integrate AI into security operations in ways that scale with data, context, and time.
That advantage, however, is neither permanent nor guaranteed. The next phase will not be decided by who adopts the most powerful models, but by who adapts their security architecture fastest. Behavioural analysis, continuous verification, and AI-native governance will become essential as content becomes unreliable and autonomous systems take on more responsibility.
Defenders will only win in 2026 if they recognise that the real challenge is no longer AI versus human, but unmanaged autonomy versus deliberate, observable control.
