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AI use is spreading through cybersecurity teams, but the evidence does not show a universal budget crisis or an inevitable hiring surge. The clearest warning is a readiness gap: many teams are using AI, while fewer have integrated it into defined workflows or mature production operations. Leaders should track AI costs, security budgets, staffing, training, governance, and validation separately rather than treating them as one trend.
What the 2026 evidence says—and what it doesn’t
Several 2026 surveys point to rapid AI uptake in cybersecurity. SANS Institute’s 2026 SOC survey found that 79% of surveyed security operations centers (SOCs) use AI or machine-learning tools, but only 36% had integrated them into a defined workflow. The survey covered 444 practitioners and 69 cyber leaders across multiple sectors and regions. Tool use, in other words, is not the same as operational integration.
A separate SANS 2026 AI survey found that active AI use in cybersecurity rose from 50% in 2025 to 78% in 2026. Yet just 27% of practitioners described their organization’s deployment as mature production. That survey included 536 global cybersecurity and IT practitioners, as well as a separate group of 57 senior security leaders. SANS sums up the gap this way: “AI adoption is moving faster than AI security.”
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These findings support a conclusion about pace and maturity—not a claim that every organization has reached “critical mass.” The studies have different populations and questions, and their percentages are not directly interchangeable. They are survey results, not audited measures of all organizations or proof that AI use caused a particular budget or staffing outcome.
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Are organizations over budget on AI?
In ETR’s Summer 2026 Macro Views Survey, 47% of organizations said their AI spending was moderately or substantially over plan. That is a significant reported budget-control issue in the survey, but it does not mean that 47% of all organizations are over budget, nor does it specify a single cause or dollar amount.
ETR also found that 18% of respondents were pulling money from cybersecurity budgets to pay for AI overruns. The finding argues against assuming that AI expansion is routinely being funded by cutting security: in this survey, that was not the common reported source. ETR identified cybersecurity as the fastest-growing software category in its survey, with growth of 7.8%; this is the survey’s category finding, not a forecast for every company’s spending.
Cybersecurity budget growth is a separate measure. ISG’s 2026 Cybersecurity Report found average enterprise cybersecurity budgets had increased 5% from 2025 to 2026. Its survey, conducted in April and May 2026, primarily covered large enterprises in the Americas and Europe. Respondents allocated about half of cybersecurity budgets to internal investments, nearly 34% to managed security service providers (MSSPs), and just over 17% to outside consulting and project support. Those proportions are rounded and may not total exactly 100%.
ETR’s AI-overrun results and ISG’s cybersecurity-budget results can coexist: organizations may exceed plans for AI while also increasing cybersecurity budgets. The surveys do not establish a universal funding pattern or show that one trend caused the other.
Does rising AI use mean security teams will hire more?
Staffing pressure is evident, but a net hiring surge is not established. SANS’s 2026 SOC survey found that only 32% of respondents said management prioritizes SOC hiring and retention. Its broader 2026 AI survey found that 73% of practitioners said AI had changed team training requirements, up from 51% in 2025. That points to changing skill needs; it does not, by itself, establish how many additional people employers will hire.
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. ENISA described the situation plainly: “The cyber talent crunch shows no signs of easing.” At the same time, the study reported spending shifting toward technology and services rather than expansion of internal teams. Its figures therefore describe recruitment and retention difficulty alongside a blended resourcing pattern, not proof of uniform headcount growth.
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ISG found that organizations use MSSPs to address threat complexity, skills or staffing gaps, and the need for 24-hour monitoring. Outsourcing can add specialist capacity or continuous coverage, but the survey does not suggest that every organization should replace internal staff with a provider. The appropriate mix depends on the work that must be owned in-house, the skills available, and the coverage required.
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What leaders should check before changing budgets or headcount
Use the survey findings as prompts for an organization-specific review, not as a forecast. These checks synthesize the issues raised across the cited studies; they are not a universal purchasing formula.
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- Operational maturity: Determine whether AI is available to individual analysts, embedded in repeatable workflows, or operating as a mature production capability. Define who approves workflow changes and how analysts handle an incorrect or incomplete result.
- Governance and validation: Identify who owns each system, what data it can access, and how performance and failures are reviewed. SANS reported that 76% of security teams held a governance role for enterprise AI, while more than half reported no formal audit framework. In the same survey, 63% reported significant AI shortcomings in threat detection and response, up from 45% in 2025. These results make validation and escalation controls practical concerns, not optional paperwork.
- Budget accountability: Compare AI spending with an approved plan, record any overrun and its funding source, and connect spending to a defined operational outcome. Track cybersecurity funding separately so an AI variance does not obscure changes to security capacity.
- Workforce model: Separate hiring needs from retention, training, outside expertise, and 24-hour coverage. Identify which capabilities can be developed internally and which gaps are difficult to recruit for or sustain.
- Coverage and visibility: Check whether teams can see and correlate relevant enterprise, cloud, operational-technology, and Internet-of-Things assets and alerts before deciding that the answer is another tool or more staff.
SANS also found a gap between operational use and budget-informed decision-making: 74% of cyber leaders said they apply threat intelligence to operations, but 26% said they use it to inform budget decisions. That makes it worth asking whether operational evidence—such as what is detected, missed, or escalated—is actually being brought into funding discussions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose the response that matches the gap
Hiring, upskilling, managed services, and tool investment solve different problems. A team may need more than one, but should identify the primary gap before committing resources.
| Approach | Most relevant when | What to verify |
|---|---|---|
| Internal hiring | A persistent capability must be owned and operated within the organization. | Whether the role can be recruited and retained, and whether management is prioritizing that work. |
| Upskilling | Existing staff need new skills to assess, govern, validate, or operate AI-enabled workflows. | Which training needs have changed and how the new capability will be applied on the job. |
| Managed services | The organization needs specialist skills, help with staffing gaps, or 24-hour monitoring. | Which responsibilities remain internal and how provider work connects to the organization’s response process. |
| Tool investment | A defined operational need is not met by current capabilities. | Workflow integration, governance, data access, validation, and the cost against an approved plan. |
This distinction matters because adding a tool does not automatically close a skills gap, and adding headcount does not guarantee that AI is governed or integrated. Matt Bromiley, a SANS Certified Instructor and author of the 2026 SANS AI Survey report, put the human role this way: “You can’t fix these gaps without people who can catch what the tools miss,”
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Deloitte’s survey of 3,235 senior leaders across 24 countries, conducted in August and September 2025, offers wider enterprise context but predates the 2026 surveys. It found that 42% considered their AI strategy highly prepared, while leaders felt less prepared in infrastructure, data, risk, and talent. Those results reinforce the need to assess operational foundations alongside adoption, but should not be presented as a 2026 measurement.
Taken together, the available evidence points to a practical priority: make AI use controllable and useful while keeping budgets and staffing decisions grounded in the organization’s actual needs. It supports close scrutiny of overruns, training, governance, validation, and sourcing. It does not support treating a universal budget reckoning or security hiring surge as settled fact.
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