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February 28, 2026

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Global DPAs joint statement on AI-generated imagery

Regulatory Alert — Global joint statement on AI-generated imagery
🛡️ Regulatory Alert · February 2026

Global DPAs warn on AI-generated imagery
& non-consensual content safeguards

A joint statement led by the Office of the Privacy Commissioner of Canada and 60+ data protection and privacy authorities signals rising expectations for deepfake prevention, privacy-by-design, and rapid remediation.

🌐
23 Feb 2026
Primary source: OPC Canada news release on the global joint statement
⚠️
High harm
Non-consensual deepfakes raise privacy, dignity, and child safety risks
🛡️
6 min
Practical actions for DPOs, privacy counsel, and product teams
📅 February 2026 ✍️ DPO Advisors ⏱️ 6 min read AI GLOBAL DPAS SAFETY
⚠️
Action required. Treat AI-generated non-consensual imagery as a priority privacy and safety risk. Update product controls and evidence-ready governance now.
01 — Background

What regulators are signaling

On 23 February 2026, the Office of the Privacy Commissioner of Canada announced a global joint statement issued with 60+ privacy and data protection authorities on AI-generated imagery. The statement calls on organisations developing or using content generation systems to implement safeguards from the outset and engage proactively with regulators.

Signal
AI-generated imagery can involve personal information and create serious privacy harms when used to produce non-consensual content.
Risk
Authorities explicitly highlight heightened impact for children and vulnerable individuals.
Expectation
Build safeguards into design, not as a post-incident patch.
Governance
Coordinate privacy, security, and safety teams on one control framework and escalation path.
This week
Reassess generative AI features for misuse scenarios: impersonation, intimate imagery, and harassment.
Evidence
Be able to demonstrate safeguards: design decisions, testing results, monitoring, and response metrics.

🔍 Safeguards regulators will expect to see

Controls
A practical, defensible baseline for AI imagery risk management
🧠
Model & policy guardrails
Restricted generation categories, prompt and output filtering, rate limits, and abuse detection.
🧾
Accountability & records
Documented risk assessment, control ownership, audit logs, and vendor oversight for AI tooling.

📊 Likely supervisory focus (qualitative)

Privacy-by-design safeguardsHigh
Engineering controls and documented testing
Child safety and vulnerable personsHigh
Higher expectations for high-harm use cases
Incident response & takedownMed-High
Speed, escalation, and evidence preservation
Transparency & user controlsMedium
Reporting, appeals, and clear notices
02 — Operating model

Design for cross-surface abuse and fast remediation

The hardest part is operational. Non-consensual content spreads across devices, channels, and reposts. Your controls must work across the full lifecycle: input, generation, storage, sharing, and removal.

🔑 Core principle: treat AI imagery systems as both a privacy and safety capability. Controls must be measurable, testable, and supported by incident response.

📱 Where controls must hold end-to-end

✅ Safeguards applied across surfaces
📱
Creator
Prompt + inputs
→
🧠
Generation
Filters + logging
→
🌍
Distribution
Sharing + takedown
  • 🔒
    Gate high-risk generation. Add friction and verification for intimate or identity-based outputs.
  • 🧪
    Test safeguards. Use red-teaming and abuse testing as part of release criteria.
  • 🧾
    Log and retain evidence. Maintain audit logs for prompts, policy blocks, and escalations with access controls.
  • 🚨
    Run a takedown playbook. Define SLAs, escalation tiers, and support flows for impacted individuals.
  • 🤝
    Vendor oversight. Align obligations with AI providers on misuse prevention and incident cooperation.
03 — Action plan

Four concrete actions to take now

Use this statement as an early indicator of convergence. Even where laws differ, regulator expectations are aligning around safeguards, evidence, and remediation capabilities.

ACTION 01
🗺️
Map AI imagery data flows
Identify where personal information enters the pipeline: training, prompts, uploads, generated outputs, and sharing.
ACTION 02
🧯
Implement a high-harm control set
Define prohibited categories, automated blocking, escalation review, and role-based access to sensitive tooling.
ACTION 03
📋
Evidence-ready governance
Maintain DPIA-style documentation, control ownership, testing records, and monitoring metrics.
ACTION 04
🚑
Operationalise takedown and support
Establish reporting channels, SLAs, appeals, and support pathways for affected individuals.

⚠️ Three lessons for privacy teams

Lesson 1
For AI imagery, privacy-by-design must be measurable: controls, tests, and logs.
Lesson 2
Deepfake risk requires joint ownership across privacy, safety, and security.
Lesson 3
Regulators expect readiness: fast action, clear escalation, and victim support.
🛡️

Need a generative AI safeguards sprint?

DPO Advisors can help you define a defensible control baseline, update governance evidence, and align product and incident response in weeks, not months.

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DPO Advisors Published February 2026 · dpoadvisors.com · contact@dpoadvisors.com