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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteYou will not close an AI security talent gap by posting one job for an “AI security expert.” Close it by defining the specific work your AI systems create, finding out which of that work your people can already do, and then combining hiring, training, outside help and retention to cover the rest. The public evidence describes a broad cybersecurity shortage. It does not give an AI-security-only headcount, so treat this as workforce planning, not a race for a scarce job title.
What the evidence says about the gap
The figures below measure cybersecurity talent in general. None of them counts AI-security specialists, and you should not quote them that way.
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- The World Economic Forum’s 2024 Strategic Cybersecurity Talent Framework estimated a worldwide shortage of nearly 4 million cybersecurity professionals.
- The WEF’s Global Cybersecurity Outlook 2025 reported that the cyber skills gap widened 8% from 2024 to 2025. In its survey, two-thirds of organizations reported moderate-to-critical skills gaps, and only 14% were confident they had the people and skills they needed.
- The same report found that 66% of organizations expected AI to have the most significant impact on cybersecurity in the coming year, while 37% said they had processes to assess the security of AI tools before deployment. These are survey results from that report, not universal rates.
- The CISA NICCS summary of the 2023 ISC2 workforce study lists AI/ML, cloud security and Zero Trust among skills-gap areas. Read that as a 2023 finding, not a current count.
The practical point: competition for people is real, and so is the risk of waiting for a perfect hire who may not be available to you.
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Two different jobs hide under “AI security”
NIST’s Karen Wetzel put it this way in June 2025: “The cybersecurity workforce will need to be prepared to secure AI against cyberattacks and to mitigate potential cyberthreats presented by AI, including where it is used with malicious intent.” (NIST, June 12, 2025.)
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That sentence covers two needs, and a third thing often gets mixed in:
- Securing AI systems: protecting the models, data pipelines, integrations and AI tools your business builds or buys.
- Defending against AI-enabled threats: for example, attackers using AI to make phishing or other attacks more convincing.
- Using AI in security work: analysts using AI tools to triage alerts. This is a productivity matter, not a replacement for either skill above.
Decide which of these you are short on before you write any requisition. Each calls for different skills.
A five-step plan
1. Start from the AI your business actually depends on
List the systems and decisions that use or depend on AI: customer-facing chatbots, code assistants, AI features in vendor software, internal models trained on company data. For each, note what must be protected, who owns the risk, and what failure would cost. This inventory step is a sensible planning recommendation of ours; the cited workforce sources do not mandate it.
2. Describe the work before naming the job
The NIST NICE Framework (SP 800-181 Rev. 1) gives employers a shared vocabulary for cybersecurity work: work roles, tasks, knowledge and skills. Use it to write down what must be done, for example reviewing an AI deployment before launch, and the knowledge and skills that work needs. NICE work roles are not the same as job titles, so one person may cover parts of several roles. The publication is dated November 2020 and points readers to current component files, so use those current versions. NIST’s June 2025 post also discussed a proposed AI Security Competency Area, then open for public comment. Check NIST’s current NICE components for its status before relying on it.
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3. Separate a headcount problem from a skills problem
Map existing staff and processes against the work you listed. You will usually find one of three situations:
- Nobody does the work, and you have no capacity: a headcount gap.
- People have capacity but lack specific knowledge, such as how to test a model integration: a skills-development gap.
- The skills exist but are not applied, because AI projects launch without security review: a process gap.
NIST’s Workforce Management resource page (updated September 24, 2026) curates employer material on job descriptions, performance-based assessment, hiring, upskilling and retention. It is a good place to find assessment approaches that test what candidates can do instead of what their CVs list.
4. Choose the mix
The WEF framework organizes action into four areas: attracting talent, educating and training, recruiting, and retaining. No single one closes every organization’s gap. Compare your options on the axes below. This comparison is our editorial synthesis, not a measured ranking.
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| Option | Time to usable capability | Fit to your systems | Knowledge stays in-house | Main risk |
|---|---|---|---|---|
| Hire | Depends on search and onboarding time | Can be high if skills are verified by assessment | Yes, while the person stays | Scarce candidates; continuity if they leave |
| Train existing staff | Gradual; builds on people who already know your environment | High for your own context, though depth in new topics takes time | Yes | Training that is not tied to real tasks fades |
| Outside services or specialists | Often quicker for a defined task such as an assessment | Varies with their grasp of your systems | Limited unless knowledge transfer is built in | Ongoing cost and dependence on availability |
For many organizations a blend is sensible: train the people who already own security, application and data work; use outside specialists for peaks or narrow tasks; and hire where a recurring need justifies a permanent role. Whatever you choose, no source supports claiming a quantified return on investment without your own data.
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5. Keep the people you build
Retention is one of the four WEF pillars, and trained staff are exactly the people who get recruited elsewhere. Give people clear career paths, real responsibility for AI-related work, and time to keep learning. Document the review procedures so capability does not walk out with one individual.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Skills to look for
The sources do not define a single authoritative AI-security skills list, so use your step 2 task list as the basis. Typical areas, drawn from the two-direction need above, include:
- Core security fundamentals: identity and access, cloud, data protection, incident response. Strong generalists are a sound base to train from.
- Understanding how AI components are built, connected to data and deployed, enough to review them.
- Assessing the security of AI tools before deployment. Only 37% of organizations in the WEF survey reported having such processes.
- Recognizing and responding to AI-enabled attacks.
- Communicating risk to the business owners who decide whether to launch.
Measure whether the gap is shrinking
Pick a few measures tied to your own priorities. Examples: the share of prioritized AI work with a named, qualified owner; time to address findings from AI reviews; the percentage of AI deployments reviewed before going live. These are suggested management measures, not metrics prescribed by NIST or the WEF. Revisit your task list and assessment results regularly, since both the technology and the NICE components keep changing.
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