Cyber defence is entering its 2026 year with the balance of advantage increasingly being determined by speed rather than sophistication. With the window between intrusion and impact now measured in minutes rather than days instead of days, the advantage is increasingly being gained by speed.
As breakout times fall below an hour and identity-based compromise replaces malware as the dominant method of entry into enterprise environments, threat actors are now operating faster, quieter, and with greater precision than ever before.
By making use of artificial intelligence, phishing, fraud, and reconnaissance can be executed at unprecedented scales, with minimal technical knowledge, which is a decisive accelerator for the phishing, fraud, and reconnaissance industries. As a result of the commoditization, automation, and availability of capabilities once requiring specialized skills, they have lowered the barrier to entry for attackers dramatically.
There is an increased threat of "adaptive, fast-evolving threats" that organizations must deal with, and one of the main factors that has contributed to this is the rapid and widespread adoption of artificial intelligence across both offensive and defensive cyber operations. Moody's Ratings describes this as leading to a "new era of adaptive, fast-evolving threats".
A key reality for chief information security officers, boards of directors, and enterprise risk leaders is highlighted in the firm's 2026 Cyber Risk Outlook: Artificial intelligence isn't just another tool in cybersecurity, but is reshaping the velocity, scale, and unpredictability of cyber risk, impacting both the management, assessment, and governance of cyber risks across a broad range of sectors.
While years have been spent investing and innovating in enterprise security, the failure of enterprise security rarely occurs as a consequence of a lack of tools or advanced technology; rather, failure is more frequently a result of operating models that place excessive and misaligned expectations on human defenders, forcing them to perform repetitive, high-stakes tasks with fragmented and incomplete information in order to accomplish their objectives.
Modern threat landscapes have changed considerably from what was originally designed to protect static environments to the dynamic environment the models were built to protect. Attack surfaces are constantly changing as endpoints change their states, cloud resources are continually being created and retired, and mobile and operational technologies are continuously extending exposures well beyond traditional perimeters.
There has been a gradual increase in threat actors exploiting this fluidity, putting together minor vulnerabilities one after another, confident that eventually defenders will not be able to keep up with them.
A huge gap exists between the speed of the environment and the limits of human-centered workflows, as security teams continue to heavily rely on manual processes for assessing alerts, establishing context, and determining when actions should be taken.
Often, attempts to remedy this imbalance through the addition of additional security products have compounded the issue, increasing operational friction, as tools overlap, alert fatigue is created, and complex handoffs are required.
Despite the fact that automation has eased some of this burden, it still has to do with human-defined rules, approvals, and thresholds, leaving many companies with security programs that may appear sophisticated at first glance but remain too slow to respond rapidly, decisively, in crisis situations.
Various security assessments from global bodies have reinforced the fact that artificial intelligence is rapidly changing both cyber risk and its scale.
In a report from Cloud Security Alliance (CSA), AI has been identified as one of the most important trends for years now, with further improvements and increased adoption expected to accelerate its impact across the threat landscape as a whole.
It is cautioned by the CSA that, while these developments offer operational benefits, malicious actors may also be able to take advantage of them, especially through the increase of social engineering and fraud effectiveness.
AI models are being trained on increasingly large data sets, making their output more convincing and operationally useful, and thus making it possible for threat actors to replicate research findings and translate them directly into attack campaigns based on their findings.
CSA believes that generative AI is already lowering the barriers to more advanced forms of cybercrime, including automated hacking as well as the potential emergence of artificial intelligence-enabled worms, according to the organization.
It has been argued by David Koh, Chief Executive of the Cybersecurity Commissioner, that the use of generative artificial intelligence brings to the table a whole new aspect of cyber threats, arguing that attackers will be able to match the increased sophistication and accessibility with their own capabilities.
Having said that, the World Economic Forum's Global Cybersecurity Outlook 2026 is aligned closely with this assessment, whose goal is to redefine cybersecurity as a structural condition of the global digital economy, rather than treating it as a technical or business risk. According to the report, cyber risk is the result of convergence of forces, including artificial intelligence, geopolitical tensions, and the rapid rise of cyber-enabled financial crime.
A study conducted by the Dublin Institute for Security Studies suggests that one of the greatest challenges facing organizations is not the emergence of new threats but rather the growing inadequacy of existing business models related to security and governance.
Despite the WEF's assessment that the most consequential factor shaping cyber risk is the rise of artificial intelligence, more than 94 percent of senior leaders believe that they can adequately manage the risks associated with AI across their organizations. However, fewer than half indicate that they feel confident in their ability to manage these risks.
According to industry analysts, including fraud and identity specialists, this gap underscores a larger concern that artificial intelligence is making scams more authentic and scaleable through automation and mass targeting. These trends, taken together, indicate that organizations are experiencing a widening gap between the speed at which cyber threats are evolving and their ability to identify, respond, and govern them effectively as a result.
Tanium offers one example of how the transition from tool-centered security to outcome-driven models is taking shape in practice, reflecting a broader shift from tool-centric security back to outcomes-driven security. This change in approach exemplifies a growing trend of security vendors seeking to translate these principles into operational reality.
In addition to proposing autonomy as a wholesale replacement for established processes, the company has also emphasized the use of real-time endpoint intelligence and agentic AI as a method of guiding and supporting decision-making within existing operational workflows in order to inform and support decision-making.
The objective is not to promote a fully autonomous system, but rather to provide organizations with the option of deciding at what pace they are ready to adopt automation.
Despite Tanium leadership's assertion that autonomous IT is an incremental journey, one involving deliberate choices regarding human involvement, governance, and control, it remains an incremental journey.
The majority of companies begin by allowing systems to recommend actions that are manually reviewed and approved, before gradually permitting automated execution within clearly defined parameters as they build confidence in their systems.
Generally, this measured approach represents a wider understanding of the industry that autonomous systems scale best when they are integrated directly into familiar platforms, like service management and incident response systems, rather than being added separately as a layer.
Vendors are hoping that by integrating live endpoint intelligence into tools like ServiceNow, security teams can shorten response times without requiring them to reorganize their operations.
In essence, this change is a recognition that enterprise security is about more than eliminating complexity; it's about managing it without exhausting the people who need to guard increasingly dynamic environments.
In order to achieve effective autonomy, humans need not be removed from the loop, but rather effort needs to be redistributed.
It has been observed that computers are better suited for continuous monitoring, correlation, and execution at scale, while humans are better suited for judgment, strategic decision-making, and exceptional cases, when humans are necessary.
There is some concern that this transition will not be defined by a single technological breakthrough but rather by the gradual building up of trust in automated decisions.
It is essential for security leaders to recognize that success lies in creating resilient systems that are able to keep up with the ever-evolving threat landscape and not pursuing the latest innovation for its own sake.
Taking a closer look ahead, organizations are going to realize that their future depends less on acquiring the next breakthrough technology, but rather on reshaping how cyber risk is managed and absorbed by the organization.
In order for security strategies to be effective in a real-world environment where speed, adaptability, and resilience are as important as detection, they must evolve.
Cybersecurity should be elevated from an operational concern to a board-level discipline, risk ownership should be aligned to business decision-making, and architectures that prioritize real-time visibility and automated processes must be prioritized.
Furthermore, organizations will need to put more emphasis on workforce sustainability, and make sure that human talent is put to the best use where it can be applied rather than being consumed by routine triage.
As autonomy expands, both vendors and enterprises will need to demonstrate that they have the technical capability they require, as well as that they are transparent, accountable, and in control of their business.
Despite the fact that AI has shaped the environment, geopolitics has shaped economic crime, and economic crime is on the rise, the strongest security programs will be those that combine technological leverage with disciplinary governance and earned trust.
It is no longer simply necessary to stop attacks, but rather to build systems and teams capable of responding decisively in a manner that is consistent with the evolving threat landscape of today.
