AI to reshape SOCs in 2026 with new risks & demands

Cybersecurity vendor Gurucul expects artificial intelligence to reshape security operations in 2026, bringing new classes of outages, more elusive insider threats and fresh infrastructure constraints alongside faster detection and response.

The company's senior executives outlined six trends that they believe will define the next phase of AI adoption in security operations centres (SOCs) and in the broader security technology stack.

They predict that organisations will face growing pressure to govern automated decision-making, explain AI-driven incident response and address the physical limits of power and cooling for AI-heavy environments.

AI-triggered outages

Steve Holmes, Senior Product Manager at Gurucul, said the growing use of AI for automated response in SOC workflows carries a risk of what he calls "self-inflicted outages".

He warned that AI systems can act with confidence but without business context. Automated systems can lock out key authentication pathways or shut down critical operations when they interpret patterns as anomalous or malicious.

Holmes expects boards and security leaders to become less tolerant of unexplained AI decisions that disrupt business operations. He said organisations will need human oversight baked into response processes and formal governance that addresses AI-triggered downtime.

"In 2026, companies will need to stop accepting 'the AI did it' as an excuse and formalize human-in-the-loop governance to prevent AI-triggered business downtime," said Holmes, Senior Product Manager, Gurucul.

Insiders using AI

Holmes also forecast a shift in insider threat behaviour as adversaries look inward at corporate AI tools and internal access models.

He expects malicious insiders to move the riskiest elements of their activity onto AI systems. These systems can automate tasks that were previously manual and more visible, such as data exfiltration, reconnaissance and privilege escalation.

Holmes said this will reduce the "noisy footprint" that traditional insider threats create. It can also complicate investigations and attribution as AI absorbs more of the operational burden of an attack.

He added that the definition of an insider will evolve. AI copilots, digital employees and autonomous agents will join human users as actors with access and potential for abuse in enterprise systems.

"2026 is the year insider threats become AI-augmented by default. And what's more, insiders will no longer be only human; we'll also see the rise of AI copilots, digital employees, and autonomous agents in this mix," said Holmes.

From alerts to stories

On the security monitoring side, Nagesh Swamy, Product Marketing Manager at Gurucul, expects a shift in how security information and event management (SIEM) products present threats.

He said SIEM platforms that issue isolated alerts are losing ground to systems that assemble "threat stories". These systems use AI to correlate identity information, behavioural data, asset context and timelines.

This produces narrative-style views of attacks rather than fragmented event streams. Swamy believes this model will expose the limitations of event-centric SIEM tools and will become the default expectation from buyers.

"By 2026, story-first analytics will no longer be a differentiator; it will be table stakes," said Swamy.

Explainable incident response

Swamy also pointed to the growth of predictive incident response playbooks that rely on AI models. These systems can anticipate and act on likely attack paths and risky behaviour.

He said this approach is already cutting containment times. It is also creating new scrutiny around accountability when models flag employees, lock down resources or trigger early containment.

Swamy expects regulators to focus more on AI compliance and transparency. He said security teams will have to explain and audit the logic behind automated actions within incident response workflows.

"As a result, 2026 will see the rise of explainable, auditable IR workflows, especially as federal regulators accelerate AI compliance and transparency," said Swamy.

Next-generation SIEM features

Looking at product design, Chris Scheels, VP, Product Marketing at Gurucul, said several AI-related features will become standard within next-generation SIEM platforms.

He expects data pipeline management and AI SOC analysts to shift from optional add-ons to core bundled elements by the end of 2026. Buyers will look for native AI support at each tier of the SOC stack rather than separate bolt-on tools.

Scheels said vendors that cannot offer integrated AI within a unified platform risk losing market share as customers consolidate tooling and look for more consistent operational outcomes.

"By the end of 2026, Data Pipeline Management (DPM) and AI SOC Analysts will no longer be 'nice to have' add-ons; they'll become core, bundled components of next-gen SIEMs," said Scheels.

Power as constraint

Scheels also highlighted physical infrastructure as a looming constraint on AI-based cyber defence. He said the rapid growth in AI model size and the increasing compute intensity of SOC workloads are putting pressure on power consumption and cooling.

He described energy availability and resilience as a cybersecurity risk in their own right as organisations scale AI deployments. Investment decisions will need to factor in how AI infrastructure is powered and protected against disruption.

Scheels expects this pressure to spur activity in new forms of energy generation, including micro-nuclear and non-nuclear technologies, aimed at supporting dense AI and data centre workloads.

"With new AI resiliency concerns tied to energy availability, expect massive innovation and rapid development of micro-nuclear and non-nuclear power generation technology," said Scheels.