What CISOs Really Need From AI in 2026 — Not Another Copilot

If you scroll through most cybersecurity product launches over the last two years, the narrative is remarkably consistent.

Everything is framed around an “AI assistant.”

 

The assumption is simple: if security teams are overwhelmed, the solution must be better explanations.

But talk to any CISO for more than ten minutes, and a very different picture emerges — often the exact opposite of what the market is building.

CISOs don’t want another copilot.
They don’t want a chatbot.
They don’t want commentary layered on top of already noisy systems.

What they want is AI that meaningfully reduces workload, closes blind spots, and operates at the same machine speed as modern attacks — attacks that have already moved beyond human-scale operations.

 

 

In 2025, the first verified case of an autonomous AI agent conducting the majority of a real espionage operation was documented.

This was not a simulation or a controlled experiment. It was a live operation in which a state-linked group delegated most technical actions to an AI agent capable of reconnaissance, exploitation, lateral movement, and large-scale data processing.

That single event crystallized something CISOs had been sensing for years but rarely articulated so clearly:

Attackers have automated. Defenders largely have not.

By 2026, this gap will no longer be theoretical. It will be operationally impossible to ignore.

This article outlines, in practical terms, what CISOs actually need from AI in the coming year, why current approaches fall short, and how AI must evolve if it is going to become a meaningful part of modern cyber defense.

 

One of the most persistent misalignments between cybersecurity vendors and security leaders is the belief that more visibility automatically leads to better defense.

Every year, SOC teams ingest more data. They collect more logs, generate more alerts, deploy more dashboards, subscribe to more threat feeds, and surface more “insights.”

And every year, SOC performance continues to degrade.

The reason is straightforward: volume without prioritization is not visibility. It is noise.

Modern SOC teams are overwhelmed by thousands of daily alerts, dozens of overlapping tools, correlation rules tuned by vendors rather than real environments, and workflows that force analysts to spend the majority of their time on low-value triage.

The limiting factor is no longer data availability.
It is human attention.

Yet most AI features introduced today increase cognitive load rather than reducing it. They summarize alerts, explain logs in natural language, or rephrase existing information without changing the underlying workload.

CISOs are explicit about what needs to change.

AI in 2026 must reduce the number of decisions humans are forced to make — not introduce new ones.

What they expect instead is AI that collapses thousands of alerts into a small number of real incidents, automatically suppresses false positives, enriches events without analyst involvement, correlates identity, endpoint, cloud, and network activity, and highlights anomalies that have actual business impact.

Most importantly, they want AI that can tell an analyst, with confidence, this is the part that matters.

What they do not want is noise rebranded as visibility, dashboards with AI labels, verbose descriptions of low-quality events, or yet another feed to integrate and maintain.

The requirement is simple and uncompromising:

 

 

Until recently, even advanced adversaries were constrained by human speed. Humans had to run scans, escalate access, navigate environments, test business logic, and manually analyze stolen data.

That constraint disappeared with the emergence of practical, adaptable AI attack agents.

By late 2025, multiple threat intelligence groups had documented attacker workflows that included autonomous reconnaissance, parallel vulnerability testing, instant adaptation when blocked, complex lateral movement chains, automated data classification, business-logic probing, and large-scale identity testing.

These are not theoretical capabilities. They are active, real-world attacker behaviors being observed today.

This fundamentally breaks the traditional SOC model.

Humans work in shifts. AI does not.
Humans get tired. AI does not.
Humans investigate step by step. AI explores multiple paths in parallel.
Humans reason sequentially. AI processes context instantly across domains.

A SOC built around manual triage, static correlation rules, signature-based detection, and event-by-event investigation is structurally slower than an AI-enabled attacker — by design.

CISOs understand this. That is why they are no longer asking for more analysts or better dashboards. They are asking for machine-speed defense.

 

When CISOs talk about AI in private, away from marketing language, their expectations are remarkably consistent. They are grounded in operational reality, not feature checklists.

AI That Does the Work — Not Narrates It

Security leaders are exhausted by AI features that describe what is happening without changing outcomes.

They do not want explanations.
They want completed steps.

That means AI capable of fully triaging initial alerts, correlating across systems before escalation, gathering all relevant context automatically, proposing likely root causes, and identifying probable attacker paths.

The bar has shifted from “assist me” to “do this step for me.”

Behaviour-Based Detection Over Signatures

AI-driven attacks do not match historical indicators of compromise.

Payloads are unique. Identity-based compromise requires sequence analysis. Business-logic attacks lack clear indicators. API abuse often looks indistinguishable from legitimate traffic. Lateral movement is hypothesis-driven, not signature-driven.

As a result, CISOs are prioritizing identity and privilege anomaly detection, behavioral modeling of users and services, access-sequence deviation analysis, business-logic baselining, machine-learning-driven session analysis, and event clustering based on context rather than keywords.

AI must understand patterns of behavior, not strings of text.

Automation for All Low-Value Work

There is broad consensus on one point: AI should automate everything that does not require human judgment.

This includes log enrichment, event stitching, suppression of recurring benign events, initial investigation, hypothesis generation, automated pivoting across related data, and safe response actions such as isolation, session revocation, forced MFA, or token invalidation.

Humans should make decisions.
Machines should handle everything else.

Fewer Tools, Stronger Telemetry Pipelines

CISOs are increasingly frustrated with environments that require integrating dozens of tools, normalizing inconsistent data, managing duplicate alerts, maintaining fragile connectors, and navigating vendor lock-in.

What they want instead is unified data collection, clean normalization pipelines, and AI operating on top of a consistent, reliable data layer.

One SOC brain, not twelve disconnected dashboards.

AI without high-quality telemetry is ineffective, and CISOs are acutely aware of this.

Explainability and Auditability

With new AI regulations emerging across the EU, UK, US, and APAC, explainability is no longer optional.

CISOs expect model transparency, full input and output logging, reproducibility of decisions, fallback models, multi-model scoring, drift detection, and complete audit trails.

In regulated industries, particularly financial services, AI that cannot be audited simply cannot be deployed.

 

 

Despite all of this, most cybersecurity vendors will continue to focus on copilots, chatbots, summarization layers, AI badges added to legacy products, and natural-language log search features.

They will do so because these approaches are easy to build, easy to demo, and easy to sell.

But CISOs do not buy demos.

They buy reduced workload, lower risk, faster response times, regulatory defensibility, and resilience against automated attacks.

The gap between what the market ships and what security leaders actually need has never been wider.

Increasingly, CISOs will choose platforms that deliver real AI capability rather than cosmetic AI features.

 

The SOC of 2026 will be defined by three core characteristics.

First, it will be data-driven, with complete and unified telemetry spanning endpoints, cloud and SaaS APIs, identity systems, network flows, DNS, access patterns, and — in some environments — on-chain activity. Without a strong data pipeline, even the most advanced models will fail.

Second, it will be AI-assisted in a substantive way. Not chatbots or search bars, but model-driven analysis that performs event clustering, cross-asset correlation, anomaly scoring, identity sequence analysis, risk-based prioritization, and real-time hypothesis generation. AI should help analysts investigate attacks, not individual events.

Third, it will be autonomous-ready. AI will take responsibility for enrichment, context gathering, suppression, stitching, initial investigation, and low-risk remediation actions. Humans will remain in control, but AI will remove friction from every step that does not require judgment.

This is the architecture CISOs are converging on.

 

CISOs have been consistent for years.

They do not want more dashboards.
They do not want more alerts.
They do not want more AI-generated descriptions of logs.

They want capacity, clarity, and speed.

Attackers have already automated reconnaissance, privilege misuse, lateral movement, API abuse, identity attacks, and fraud workflows.

Defenders, by contrast, are still automating commentary.

In 2026, AI must evolve from assistant to analyst, from explainer to operator, from advisor to executor, and from suggestion to action.

AI will not replace security teams.
But it must replace the repetitive work that prevents those teams from focusing on what truly matters.

Attackers are using AI to scale.

Defenders must use AI to survive.

This is what CISOs really want in 2026 — not another copilot.