State of Privacy 2026: Staffing, Training and Budget Pressures

Privacy teams face growing demands on limited resources.




ISACA’s State of Privacy 2026 report (survey run in Sept 2025 with 1,800+ respondents) reports that privacy risk is rising while privacy capacity is shrinking.

This review focuses on staffing, training and the changes privacy teams can make with limited resources.

1) Privacy staffing has declined

The survey reports:

  • The median privacy staff size dropped to 5, down from 8 last year.
  • 11% of organizations have only one full-time person handling privacy.

Smaller teams still need to assess AI use and respond to data subject requests.

Practical implications

Treating privacy as a separate review at the end of a project can add delays.

Lower staffing makes those delays harder to manage.

If privacy isn’t built into how the business runs, you end up with:

  • constant fire-fighting
  • checkbox compliance
  • exhausted teams
  • and unresolved control gaps

2) Understaffing is worse on the technical side

ISACA separates privacy staffing into two categories:

  • Legal/compliance privacy roles
  • Technical privacy roles (privacy engineering, implementing controls, tooling, automation)

Both are under pressure. But technical privacy roles are consistently harder to cover—because you need people who can translate privacy requirements into actual system behavior.

And the survey also shows technical privacy roles are slightly more likely to see increased demand in the next year.

Practical implications

Allocate time and staff to implementing privacy controls.

Priorities include:

  • data mapping that actually stays updated
  • automated evidence collection
  • scalable DSAR handling
  • privacy-by-design embedded in SDLC
  • AI reviews with assigned owners and recorded decisions

3) Hiring criteria include adaptability

ISACA asked what makes a privacy candidate “qualified.” The top answers were:

  1. Organizational fit (culture)
  2. Hands-on privacy experience
  3. Adaptability

Notably, “organizational fit” and “adaptability” were new options this year—and they jumped straight to the top.

Also:

  • Expert-level privacy professionals are the hardest to hire (by far)

Practical implications

Privacy roles require continuing professional development.

Staff need to keep up with changes in tools, laws and AI use.

When hiring, assess how candidates apply their knowledge and work with other teams.

4) The #1 privacy failure is still training (and it’s getting worse)

This was one of the most blunt findings in the report:

  • Lack of training / poor training is the most common privacy failure (51%).
  • The next most common failure: not practicing privacy by design (and it jumped noticeably vs last year).
  • Data breaches / leakage are also high on the list.

And while 79% of organizations provide privacy awareness training, the existence of training clearly isn’t the same as effective training.

Practical implications

Training should have a defined behavioural objective.

Attendance records alone do not show whether staff can apply what they learned.

If your program is just:

  • annual slides
  • a quiz
  • a completion report

check whether staff can apply the lessons in their work.

The real question is: What behavior changed this quarter because of training?

5) Board support and privacy priorities

  • 56% said their board adequately prioritizes privacy.
  • Boards most commonly view privacy programs as compliance-driven (the largest share).

ISACA also draws a strong connection between board support and:

  • practicing privacy by design
  • confidence in compliance outcomes
  • adequate budget funding

Practical implications

Board discussions should explain the operational effects of privacy risks.

Use evidence when requesting resources.

Explain the potential business consequences:

  • revenue risk
  • market access
  • customer churn
  • product delays
  • incident costs
  • and AI-related reputational risk

6) AI adoption in privacy is real… but it’s not mainstream yet

The survey shows:

  • A relatively small slice currently uses AI for privacy tasks
  • Some plan to adopt within 12 months
  • A large share still has no plans

ISACA’s important point: AI adoption correlates with maturity.
Organizations with stronger privacy-by-design habits and board prioritization are more likely to use AI responsibly.

Practical implications

AI tools need defined uses and oversight.

Establish an inventory, responsibilities, workflows and measures before introducing AI tools.

Potential uses, subject to review, include:

  • classify personal data
  • draft DPIAs and risk narratives (with human review)
  • accelerate policy mapping
  • support DSAR triage
  • detect privacy issues in requirements or tickets

7) Budget optimism is fading — even if cuts haven’t fully landed yet

ISACA notes a clear trend:

  • fewer than a quarter expect budgets to increase
  • about half anticipate decreases

Even when actual cuts lag behind expectations, the fear of cuts changes behavior:

  • hiring freezes
  • delayed tooling
  • no training refresh
  • “make do with what we have”

Practical implications

With limited resources, privacy teams can improve operational efficiency through:

  • standard workflows
  • strong templates
  • metrics
  • automation where it’s safe
  • clear ownership across teams

Assign privacy responsibilities within existing business workflows.

A practical plan for 2026

Privacy leads can adapt the following steps to their teams:

1) Define privacy workflows and responsibilities

  • Define intake (projects, vendors, DSARs, incidents)
  • Set SLAs
  • Assign ownership per workflow
  • Track throughput

2) Put privacy-by-design into the SDLC

  • Add privacy checkpoints into product rituals (PRD, design review, sprint grooming)
  • Require DPIA triggers for high-risk changes (especially AI)

3) Fix training to target behavior

  • short, role-based modules (marketing ≠ engineering ≠ HR)
  • measure impact using more than “completion”
  • connect it to real incidents and real scenarios

4) Give leadership a privacy dashboard

If you can’t show:

  • incident trends
  • DSAR volume and SLA
  • DPIA volume
  • vendor risk status
  • top risks and mitigation progress

leadership will have less information for resource decisions.

5) Use AI carefully, where it actually helps

Start small:

  • classification assistance + human review
  • drafting support + approvals
  • ticket triage
  • evidence summarization

And document governance: what you use, what data touches it, and what you prohibit.

Choosing the first improvement

The staffing and budget findings support a review of priorities.

Identify which tasks need technical support and which can be handled through established business workflows.

Track whether those changes reduce delays and unresolved risks.

Start with the gap that creates the most unresolved risk or repeated work.

Related guidance and practical next steps

Masoud Salmani