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AI use is spreading quickly across cybersecurity, but the evidence does not show a single, inevitable budget crisis or hiring boom. The clearest signal is a readiness gap: many security teams use AI, far fewer have embedded it in defined workflows, and reported spending overruns coexist with rising cybersecurity budgets. Leaders should plan for the capabilities and controls they need—not assume that every organization must spend or hire in the same way.
AI use is widespread; operational maturity is lagging
Two 2026 SANS Institute surveys show how quickly AI has entered security work—and why adoption figures alone can overstate readiness. In SANS’s survey of 444 practitioners and 69 cyber leaders across multiple sectors and regions, 79% of security operations centers (SOCs) reported using AI or machine-learning tools, but 36% said those tools were integrated into a defined workflow. A separate SANS AI survey of 536 global cybersecurity and IT practitioners, plus 57 senior security leaders, found active AI use in cybersecurity had risen from 50% in 2025 to 78% in 2026. Yet only 27% of practitioners described their AI deployment as mature production.
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These measures describe different populations and questions, so they should not be combined into a single adoption rate. Together, they point to a practical distinction: access to a tool is not the same as a repeatable, governed process that produces validated results. SANS sums up the broader concern: “AI adoption is moving faster than AI security.”
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SANS found that 76% of security teams held a governance role for enterprise AI, while more than half reported having no formal audit framework. In the same survey, 63% reported significant shortcomings in AI-supported threat detection and response, up from 45% in 2025. These findings make oversight and evaluation operational priorities, not administrative extras: teams need to know what data systems can access, how results are checked, and when a person must review or escalate a result.
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That need is especially relevant when AI outputs influence security decisions. SANS Certified Instructor Matt Bromiley, author of the 2026 SANS AI Survey report, put it this way: “You can’t fix these gaps without people who can catch what the tools miss,”
AI overruns do not mean cybersecurity budgets are being cut
ETR’s Summer 2026 Macro Views Survey found that 47% of organizations had AI spending moderately or substantially over plan. But only 18% said they drew money from cybersecurity budgets to fund those overruns. In that same survey, cybersecurity was the top-growing software category, with reported growth of 7.8%. These are survey results, not a forecast for every organization; they show that AI overspending and continued cyber investment can occur at the same time.
ISG’s 2026 Cybersecurity Report, based on research conducted in April and May 2026 and focused primarily on large enterprises in the Americas and Europe, found average cybersecurity budgets had increased 5% from 2025 to 2026. Respondents allocated about half of that budget to internal investment, nearly 34% to managed security service providers (MSSPs), and just over 17% to external consulting and project support. The proportions are rounded and may not total exactly 100%.
| Finding | What it measures | Survey context |
|---|---|---|
| 47% had AI spending moderately or substantially over plan | AI spending against plan | ETR Summer 2026 Macro Views Survey |
| 18% drew from cybersecurity budgets to fund AI overruns | One reported funding source for AI overruns | ETR Summer 2026 Macro Views Survey |
| 7.8% reported growth | Cybersecurity, the top-growing software category in that survey | ETR Summer 2026 Macro Views Survey |
| 5% average increase from 2025 to 2026 | Enterprise cybersecurity budget growth | ISG 2026 Cybersecurity Report; primarily large enterprises in the Americas and Europe |
The budget question for a leadership team is therefore not simply whether AI is expensive. It is whether actual AI costs are visible against an approved plan, what outcomes justify them, and which funding lines are being moved. SANS also found a gap between operational intelligence and financial planning: 74% of cyber leaders applied threat intelligence to operations, but 26% used it to inform budget decisions.
Security teams face pressure, but a hiring surge is not established
The workforce evidence points to difficulty recruiting, retaining, and developing skills—not proof that every organization is about to expand its security headcount. In SANS’s 2026 SOC survey, only 32% said management prioritized SOC hiring and retention. SANS’s separate AI survey found that 73% of practitioners said AI had changed team training requirements in 2026, up from 51% in 2025. That is a change in training needs, not a count of new jobs.
ENISA’s 2025 NIS Investments study, covering organizations in EU Member States, found that 76% reported difficulty attracting cybersecurity professionals and 71% difficulty retaining them. It also reported a median cybersecurity investment of €1.5 million and cybersecurity spending equal to 9% of IT budgets. Those figures apply to the study’s surveyed organizations, not all businesses or regions. ENISA’s summary says, “The cyber talent crunch shows no signs of easing.”
Other findings complicate a simple headcount narrative. ETR reported that cybersecurity roles were among the least affected by reported AI-driven headcount reductions. ENISA describes spending shifting toward technology and services rather than expansion of internal teams. ISG respondents cited threat complexity, skills or staffing gaps, and the need for 24-hour monitoring as reasons for using MSSPs. The likely decision is not “hire or automate” in the abstract: it is which expertise to build internally, which coverage to source externally, and what training is required to oversee both.
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Choose a workforce and investment model around the actual gap
Hiring, upskilling, managed services, and more tooling solve different problems. Before committing funds, leaders can assess the gap across these areas:
- Operational maturity: Is AI available to individual analysts, embedded in a repeatable workflow, or operating as a mature production capability? Define what evidence would justify moving to the next stage.
- Governance and validation: Assign ownership, define permitted data access, and set checks for accuracy, failures, auditability, and human escalation. The SANS findings on audit frameworks and reported detection-and-response shortcomings make validation a concrete priority.
- Workforce model: Identify skills that can be developed internally, roles that are difficult to recruit or retain, and specialist or round-the-clock needs that may justify outside support.
- Budget accountability: Track AI spending against the approved plan and the outcomes it is meant to deliver. Make explicit whether the funding is new or moved from another category.
- Coverage and visibility: Check whether the team can see and correlate relevant enterprise, cloud, operational-technology, and internet-of-things assets and alerts before buying another tool or adding staff.
This is a way to frame the decision, not a universal staffing or purchasing formula. For broader enterprise context, Deloitte’s survey of 3,235 senior leaders in 24 countries, fielded in August and September 2025, found that 42% considered their AI strategy highly prepared, while leaders felt less prepared on infrastructure, data, risk, and talent. Because that fieldwork predates the 2026 surveys, it offers context rather than a current measure of readiness.
What leaders should conclude from the surveys
AI adoption in cybersecurity has reached broad use in the surveyed populations, but defined workflows, mature production deployment, governance, and training have not kept pace. Meanwhile, ETR’s over-plan finding does not establish that cyber budgets are being raided: its survey found that cybersecurity was an uncommon source of funds for AI overruns, and ISG separately reported average cyber-budget growth. Workforce pressure is documented, but the available findings support planning for retention, training, and blended sourcing—not a blanket prediction of a security hiring surge. Survey populations and methods differ, so the percentages are indicators of distinct trends rather than directly comparable measures or proof that AI caused any one budget or staffing outcome.
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