Introduction: A Market Defined by Two Directions at Once

The artificial intelligence in cybersecurity market covers the software, platforms, and services that use machine learning, generative models, and increasingly autonomous AI agents to detect, investigate, prevent, and remediate cyber threats, together with the tools that protect AI systems themselves. For much of the past decade the category meant statistical anomaly detection embedded in endpoint, network, and email products. It is now something broader and more consequential, a contest in which both attackers and defenders operate with machine-speed tools, and in which the AI models, agents, and data pipelines that enterprises deploy have become part of the attack surface that must be defended.

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The scale of the shift is visible in how executives describe it. In the World Economic Forum’s Global Cybersecurity Outlook 2026, 94 percent of respondents identified AI as the most significant driver of cybersecurity change in the year ahead, and 87 percent named AI-related vulnerabilities as the fastest-growing cyber risk of 2025. That combination of opportunity and exposure explains why the market is evolving along two parallel tracks, AI applied to security operations, and security applied to AI. Adopting current technology matters because the economics have changed. IBM’s 2026 Cost of a Data Breach Report found that roughly one in four malicious breaches was now AI-enabled, a 56 percent rise over the prior year, and that such breaches averaged about 6 million dollars against a global breach average of 4.99 million dollars.

Why the Market Is Accelerating: The Threat Side of the Ledger

The clearest marker of the change is Anthropic’s November 2025 disclosure that it had disrupted what it described as the first reported AI-orchestrated cyber espionage campaign. In mid-September 2025, Anthropic detected a Chinese state-sponsored group, designated GTG-1002, manipulating its Claude Code tool to attempt intrusions against roughly thirty global targets, with a handful of validated successes. The significance, in Anthropic’s account, was that the AI executed much of the operation itself rather than merely advising a human operator. The same report offered a useful corrective: the model frequently overstated findings and occasionally fabricated data, which limited full autonomy. This is a vendor’s self-reported case, but it aligns with independent government assessment. The UK National Cyber Security Centre judges that AI will almost certainly continue to make elements of intrusion operations more effective and efficient, increasing the frequency and intensity of threats through 2027, and that a digital divide will open between organizations that keep pace and a large proportion that become more vulnerable. Google’s Threat Intelligence Group reports a similar pattern, with adversaries using AI to support exploit development, reconnaissance, and social engineering across the attack lifecycle.

IBM’s 2026 research, conducted with the Ponemon Institute across 602 breached organizations, quantifies the consequences. AI-enabled attacks were dominated by deepfake impersonation and AI-enabled malware, and most of them targeted critical infrastructure sectors, with financial services and energy the most concentrated. Ransomware incidents rose to 39 percent of breaches from 34 percent, with attackers increasingly automating and scaling their operations. IBM’s framing is worth noting for its economic logic, attacks are becoming faster and cheaper to launch while breaches remain expensive to find and fix, so the decisive variable is the lag between discovery and remediation. That logic is the commercial engine behind nearly every product trend described next.

The Technologies and Trends Reshaping the Market

1. The agentic security operations centre

The most visible product trend is the move from AI assistants that answer questions to agents that take actions. Microsoft illustrated the direction at Ignite 2025 by introducing a dozen new Security Copilot agents embedded in Defender, Entra, Intune, and Purview, covering tasks such as alert triage, conditional access optimization, threat intelligence briefing, and data-security alert prioritization. More important for market structure was the commercial decision that accompanied it, Security Copilot is being included in Microsoft 365 E5 and E7 licences, with 400 security compute units per month for every 1,000 licensed users, capped at 10,000, and pay-as-you-go capacity beyond that at 6 dollars per unit. Bundling agentic capability into an existing licence changes the adoption curve, because it removes procurement friction for a very large installed base, and it pressures standalone vendors to demonstrate value beyond what a platform includes by default.

Palo Alto Networks is pursuing the same destination through acquisition. Its September 2026 purchase of Console, an AI-native platform for building agentic workflows, is intended to deepen the agentic capabilities of its Cortex security operations platform, with CEO Nikesh Arora describing the goal as a shift to “software-as-an-agent” that alerts and remediates issues automatically. The pattern across vendors is consistent, the value proposition is moving from helping analysts work faster to removing routine analyst work altogether. The workforce dimension reinforces this. In the World Economic Forum’s survey, 54 percent of respondents cited insufficient knowledge or skills as a barrier to deploying AI for cybersecurity, and 41 percent pointed to the need for human oversight. Agents promise to relieve skill scarcity, but the oversight concern shows that governance, not capability, is now the binding constraint on adoption.

IBM’s data adds a caution about where agents are actually being used. More than half of surveyed organizations reported using agents for threat detection and containment, yet only 18 percent applied them to vulnerability management, leaving known exposures to linger even as AI shortens exploit windows. For vendors, that gap is an evident product opportunity. For buyers, it suggests that automation investment is concentrated in the most visible part of the security workflow rather than the part where attackers are moving fastest.

2. Identity security for machines and AI agents

The third trend is the reframing of identity as the primary control plane once autonomous agents hold credentials and act on behalf of users. Palo Alto Networks completed its acquisition of CyberArk on February 2026, describing identity security as a core pillar of its platform strategy and promising to secure human, machine, and agentic identities. The transaction, announced in 2025 at roughly 25 billion dollars, was paid in cash and stock, with the filed consideration including 2.3 billion dollars in cash and 112 million Palo Alto Networks shares. CrowdStrike’s January 2026 agreement to acquire SGNL points the same way, with the stated aim of continuously granting and revoking access for human, non-human, and AI identities based on real-time risk. ServiceNow, meanwhile, paired identity and asset context by completing Veza in March 2026 and Armis, at approximately 7.75 billion dollars in cash, on April 2026, arguing that cyber asset intelligence is the foundation for deploying agentic AI with trust and control.

The analytical point is that identity, exposure management, and security operations are being fused around the same idea, an agent can only be trusted if the system knows what it is, what it can reach, and when its privileges should change. The browser is emerging as a related control point. CrowdStrike’s planned Seraphic acquisition targets runtime protection inside any browser, on the reasoning, which the company cites, that most of the working day is spent there and that agentic browsers and generative AI tools create new data-leakage paths.

3. Securing AI itself: shadow AI, governance, and standards

The fourth trend is the fastest-growing new segment: protecting AI models, applications, and the data that feeds them. IBM’s 2025 research found that a high level of shadow AI, meaning unapproved AI tools used by staff, added 670,000 dollars to the average breach cost, and that 97 percent of breached organizations that suffered an AI-related security incident lacked proper AI access controls. The 2026 study found that more than 20 percent of organizations had experienced a breach targeting AI models or applications, most often through compromised APIs, applications, or plug-ins and cloud misconfigurations affecting AI workloads. Notably, the weak point was typically the surrounding infrastructure rather than the model, which argues for integrating AI security with mainstream cloud and application security rather than treating it as a separate discipline. The World Economic Forum reports that data leaks associated with generative AI now rank ahead of adversarial capability advances as a leading concern for 2026, a reversal from the prior year.

Standards bodies are responding. In December 2025 the US National Institute of Standards and Technology released a preliminary draft of its Cyber AI Profile, which applies the Cybersecurity Framework 2.0 to three overlapping focus areas: securing AI systems, conducting AI-enabled cyber defence, and thwarting AI-enabled cyberattacks. Based on the material available at the time of writing, the profile remains a voluntary draft, but its structure is telling. It treats AI as simultaneously an asset to be protected, a tool to be used, and a threat to be countered, which is precisely the three-way market structure vendors are now building toward. Frameworks of this kind typically become procurement reference points long before they become regulation, and they give buyers a vocabulary for evaluating vendor claims.

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What These Developments Mean for Performance, Cost, and Adoption

The business case for AI in security rests mainly on speed and cost. IBM found that organizations using AI and automation in security operations cut breach costs by almost 2 million dollars on average, yet one in four organizations had still not adopted such tools. Cost savings from faster containment are the most defensible return on investment in the category because they are measured against actual breach outcomes rather than vendor benchmarks. Productivity gains are the second driver. The World Economic Forum reports that AI is accelerating detection, triage, and response while automating labour-intensive work such as log analysis and compliance reporting. Quality gains follow from correlation across identity, endpoint, cloud, and browser telemetry, which is what CrowdStrike, Palo Alto Networks, and Microsoft each cite as the rationale for platform expansion.

The adoption picture is uneven, and that unevenness is itself a market signal. The World Economic Forum finds that larger organizations lead in AI-driven threat detection and automation, while smaller entities, governments, and non-profits lag. The NCSC’s “digital divide” judgement points to the same risk from the threat side. Vendors that can package agentic capability at low marginal cost for mid-sized organizations, as Microsoft’s bundling does, are addressing the segment where unmet need is greatest. Customer experience, in this context, means fewer alerts, faster answers, and more automated remediation, but only if governance is designed in. IBM’s finding that 97 percent of breached organizations with AI-related incidents lacked adequate access controls is a reminder that speed without control simply moves risk elsewhere.

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Outlook: Opportunities and Constraints

Several conclusions follow from the evidence. First, the centre of gravity is moving from detection to remediation. As AI makes vulnerability discovery abundant, the winners are likely to be vendors that can verify findings, prioritize by real exposure, and close the loop with patches and access changes. IBM’s executive framing of the priority, eliminating the lag between discovery and remediation, is a fair summary. Second, identity and AI governance will grow together. Every agent deployed is a new privileged identity, and every unsanctioned tool is a new data path, so demand for non-human identity management, AI discovery, and runtime guardrails should track enterprise agent adoption. Third, government and standards guidance will increasingly shape purchasing. The NCSC’s assessment and NIST’s Cyber AI Profile give boards and regulators reference points for what “reasonable” looks like, and they are likely to influence audit and insurance expectations.

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The constraints are just as real. Trust and oversight remain barriers to adoption, skills shortages persist, and autonomous systems can err, as Anthropic’s own analysis of attacker-operated agents showed. Consolidation may deliver integrated protection, but it also concentrates dependency in a handful of platforms. Finally, much of the most striking evidence, including Glasswing’s vulnerability counts, comes from developers describing their own systems, and it will take independent evaluation to establish how well those results generalize. For decision-makers, the practical takeaway is to treat AI in cybersecurity as an operating-model change rather than a product purchase: pair automation with identity controls and AI governance, extend agents from detection into vulnerability management, and measure success by time-to-remediate rather than alert volume.