Brand Safety for AI Media: A 2025 Operational Playbook for Marketers
As a marketing lead who has deployed hybrid AI/human brand safety programs across open web, social, CTV, and retail media, I’ve learned that the difference between “we got lucky” and “we were ready” is an operational blueprint you actually use. 2025 raises the bar: the EU AI Act entered into force in August 2024 with phased applicability through 2025, including transparency obligations for general-purpose AI systems and a code of practice process under the EU AI Office, as noted in the European Commission’s 2024–2025 updates (EC AI Act entry news, 2024; EU digital strategy pages, 2025). In the U.S., the FTC tightened the screws with a final rule banning fake reviews in August 2024 and ongoing actions around AI impersonation and deceptive claims (FTC final rule on fake reviews, 2024; FTC proposed impersonation rule, 2024).
Meanwhile, the risk surface is shifting. DoubleVerify’s 2024–2025 analyses show CTV bot fraud accounting for about 65% of CTV fraud, with one bot variant capable of wasting over $7.5M per month if left unchecked (DV Global Insights highlights, 2024–2025). Integral Ad Science reported in 2024 that global brand risk fell to a record low of 1.5%—but only when brand safety is actively managed—and that fraud on non-optimized campaigns surged to 10.9% versus 0.7% on protected ones (IAS Media Quality Report – 20th ed., 2024; IAS fraud overview, 2024).
This playbook distills what consistently works in the field—actionable guardrails, settings, and workflows—so you can implement brand safety for AI media with confidence.
1) Governance First: Define Safety vs. Suitability and Who Decides
Brand safety prevents adjacency to illegal or universally harmful content; brand suitability tunes environments to your tolerance and values. With the 2024 discontinuation of GARM’s central coordination, brands now own their frameworks. Use the GARM floor and adjacency concepts as a starting point, then adapt to your context (see WFA/GARM background and 2024 priorities in the WFA Annual Report 2024).
Practical elements to include in your Brand Safety & Suitability Policy:
- Risk tiers per channel: open web, social/UGC, CTV, retail media, and AI-generated content.
- Legal guardrails: align with the FTC Endorsement Guides, the FTC’s 2024 crackdown on deceptive AI claims, and EU transparency expectations under the AI Act framework.
- Human-in-the-loop gates for AI-assisted creation: editorial review, claims substantiation, and rights clearance before publish.
Escalation Matrix (what we use in practice):
- Severity levels: S1 (legal/IP breach), S2 (reputational harm), S3 (minor suitability miss).
- Roles: Marketing Incident Owner, Legal, Agency/DSP rep, Verification vendor contact.
- SLAs: S1 triage ≤2 hours; S2 ≤1 business day; S3 ≤3 business days.
How to implement
- Draft a one-page policy addendum per channel with examples of allowed/conditional/excluded inventory and creative claims.
- Run a one-hour calibration workshop with media, creative, and legal to lock thresholds and SLAs.
- Store the policy in your DSPs’ documentation and your team runbook; require sign-off for changes.
2) Pre-Bid Controls That Prevent Most Incidents
These are the settings and filters that reliably block the majority of waste and adjacency problems before a single impression serves.
Core controls to activate in DSPs and platforms:
- Safety/suitability categories: Apply floor categories plus custom suitability lists for your sector; update quarterly.
- Fraud/SIVT filters: Enforce pre-bid fraud filtering from leading vendors and validate post-bid (see the gap between non-optimized 10.9% vs protected 0.7% fraud in 2024 per IAS global benchmarks).
- MFA and misinformation avoidance: Adopt tiered MFA pre-bid segments and reputable misinformation ratings where licensed, as aligned with DV’s 2024 MFA tiered categories.
- CTV-specific: Prefer app-level allowlists; avoid supply that is server-side ad insertion (SSAI)-only or lacks authentication; validate with post-bid CTV fraud checks given the concentration of bot activity noted by DV (CTV bot fraud focus, 2024–2025).
- Retail Media Networks: Bias buys to owned-and-operated placements, which saw materially higher viewability (around 73%) compared to audience extension (around 36%) in DV’s 2024 readouts (DV Global Insights 2024).
How to implement
- Build a master allowlist per channel and a dynamic blocklist that updates weekly from vendor and internal incident data.
- Predefine line-item templates in your DSP with safety, suitability, and SIVT filters on by default; require business justification to opt out.
- For CTV, require app transparency in IOs and add a contractual clause permitting immediate suspension if SSAI spoofing is detected.
3) AI Media Production: Disclosures, Rights, and Proof
AI accelerates ideation and production—but it increases compliance risk without discipline.
Non-negotiables for AI-generated or AI-assisted assets:
- Disclosure when material: Where AI generation would influence consumer understanding, label clearly. This aligns with EU transparency expectations (see EU AI Act implementation materials, 2025) and U.S. deception standards under the FTC’s online advertising guidance.
- IP provenance and licenses: Maintain a rights log for every asset—training data notes if available, licenses, and consents. CNIL’s 2025 recommendations emphasize informing data subjects and enabling rights in AI contexts (CNIL AI recommendations, 2025).
- Endorsements and deepfakes: No simulated endorsements or likenesses without explicit consent and clear disclosure; the FTC pursued expanded protections in 2024–2025 (FTC proposed impersonation rule, 2024).
- Claims substantiation: Fact-check claims; maintain a citations appendix and legal-reviewed claim bank.
How to implement
- Add a disclosure style guide with examples for text, video, and audio; enforce via your CMS and creative QA checklist.
- Require a provenance checklist item before asset approval (rights, consent, model used, dataset notes, disclosure label if needed).
- Run quarterly “red-team” drills to try to make your AI pipeline hallucinate or reuse protected content; document mitigation steps.
4) Real-Time Monitoring: What to Watch and When to Alert
Post-bid verification closes the loop and catches what pre-bid controls miss. Set clear thresholds and automate alerts.
Dashboards and thresholds that consistently work:
- Brand risk percent: Trigger investigation if brand risk exceeds the most recent global baseline. In 2024, IAS reported a record-low global brand risk of about 1.5%, a practical reference point for alerting (IAS MQR – 20th ed., 2024).
- SIVT/fraud percent: Alert if SIVT crosses 2% on protected inventory; escalate if patterns match known CTV or SSAI anomalies highlighted in DV’s bot research (DV insights on CTV bot fraud, 2024–2025).
- Attention/viewability: Track attention where available to understand suitability fit and optimize; DV and IAS both emphasize the rising utility of attention metrics in 2024–2025 (DV attention highlights, 2024–2025).
- Event surge protocol: During elections or crises, raise thresholds and expand blocklists; IAS observed surges in risky political content and misinformation in 2024 (IAS election-cycle insight, 2024).
How to implement
- Create alerting rules in your verification dashboards; route S1/S2 alerts to a dedicated Slack channel with on-call rotation.
- For CTV, monitor app-level performance and “TV off/zero-view” signals; escalate anomalies immediately.
- Hold weekly 30-minute reviews to evaluate trend lines against your policy targets and adjust allow/block lists.
5) Incident Response: A Runbook That Stands Up Under Pressure
When something slips through, speed and documentation protect your brand and recover spend.
The five-step runbook we’ve used successfully:
- Pause and preserve: Auto-pause offending line items; capture screenshots, log IDs, and domain/app details.
- Notify: Alert internal stakeholders and supply partners; open a ticket with your verification vendor.
- Legal triage: Assess FTC/GDPR/AI Act implications; if endorsements or disclosures are implicated, prepare corrective messaging.
- Root cause: Determine if inventory source, targeting, or creative caused the breach; update allow/block lists.
- Recovery and prevention: Pursue make-goods/credits; update your policy and templates; brief the team.
How to implement
- Keep a two-page incident template pre-filled with contacts, SLAs, and evidence fields.
- Run a quarterly tabletop exercise (60 minutes) simulating a CTV bot fraud spike or a deepfake endorsement incident.
- Track time-to-pause and time-to-resolution; aim for S1 median <2 hours to stabilize.
6) Post-Campaign Audit: Prove the Value and Tune Suitability
Audits turn experience into measurable improvement.
What to include in every audit:
- Protected vs unprotected deltas: Use fraud/SIVT baselines to quantify avoided waste; IAS reported 0.7% fraud with protections vs 10.9% without in late 2024, a useful directional benchmark for your reconciliation (IAS fraud benchmarks, 2024).
- Suitability and performance: Look for attention and conversion uplifts tied to better contextual fit. For example, OMD and Nissan saw up to 6x conversion rate lift in specific categories when applying suitability measurement and optimization in 2024 (IAS OMD/Nissan case, 2024).
- Retail media mix: Compare O&O vs extension placement outcomes; DV’s 2024 data showed substantial viewability gaps between the two (DV retail media insights, 2024).
How to implement
- Create a standard “end-of-flight” deck with three slides: (1) brand safety & fraud deltas, (2) suitability/attention learnings, (3) policy changes.
- Feed learnings into updated allowlists and creative briefs; refresh thresholds quarterly.
7) Training and Human Oversight: Calibrate People to the Machines
Tools don’t interpret themselves. Teams need shared understanding and practice.
Program elements that work:
- Role-based training: Media traders on pre-/post-bid settings; creatives on disclosures and claims; legal on AI/endorsement/impersonation rules; analysts on trend interpretation.
- Vendor calibration: Quarterly sessions with verification partners to align on scoring, thresholds, and new risk signatures.
- Red-teaming AI pipelines: Quarterly exercises to expose hallucinations, rights issues, or disclosure gaps; document mitigations and add to QA checklists.
How to implement
- Maintain a competency matrix per role with refresh dates and required drills.
- Use a 45-minute monthly “anomaly review” to inspect oddities (e.g., sudden surges in “made-for-advertising” domains).
8) Emerging Risks in 2025 and How to Stay Ahead
- Deepfakes and synthetic endorsements: Require consent and labeling where material; keep signed releases; track evolving FTC and EU obligations (FTC impersonation/deepfakes efforts, 2024; EU AI Act transparency pathways, 2025).
- Misinformation surges: Elections and crises can amplify risk; consider licensed ratings to avoid unreliable sites. NewsGuard estimated about $2.6B in annual ad spend flows to unreliable properties as of 2025, underscoring waste exposure (NewsGuard insight, 2025).
- CTV fraud vectors: SSAI spoofing and “TV off” impressions persist; DV and IAS have flagged CTV-specific anomalies; intensify verification on live events and maintain app allowlists (DV attention/CTV highlights, 2024–2025; IAS APAC live sports insight, 2024).
- MFA and low-quality supply: Use pre-bid MFA tiers and supply path optimization. DV’s 2024 tiered categories provide an actionable model (DV MFA tiers, 2024).
9) Toolbox: Verification, Misinformation Defense, and Content Governance
Disclosure: The following section includes a neutral mention of our own product for context.
Verification and measurement
- DoubleVerify (DV): Strengths in CTV fraud intel, retail media viewability and attention metrics; see 2024–2025 updates on attention and CTV bot patterns (DV Global Insights, 2024–2025).
- Integral Ad Science (IAS): Global benchmarks for brand risk and fraud, Context Control and attention solutions; note 2024 record-low 1.5% brand risk and fraud deltas (IAS MQR – 20th ed., 2024).
- HUMAN Security: Cross-environment bot/fraud defenses and threat intel for programmatic, audio, and beyond (HUMAN ad fraud solution).
- NewsGuard: Misinformation ratings and lists to reduce adjacency to unreliable news sources; 2025 waste estimate provides decision support (NewsGuard insight, 2025).
- Oracle Moat, Zefr, Pixalate: Additional measurement and suitability options (seek current 2024–2025 materials directly from vendors for the latest benchmarks).
Content governance and production
- QuickCreator: AI-powered content platform with block-based editing, multilingual generation, SEO optimization, and team workflows—useful for enforcing disclosure styles, embedding citations, and governing AI-assisted production alongside verification tools. This mention includes our own product; treat it as one option among many.
- Alternatives: Your CMS with custom QA checklists; enterprise DAMs with rights-tracking modules; lightweight editorial dashboards paired with verification vendor APIs.
Selection guidance (criteria that consistently matter)
- Coverage by channel and format (CTV, Shorts/Reels, RMNs) and by geography.
- Quality of pre-bid segments (MFA tiers, misinformation lists) and post-bid diagnostics.
- Ease of alerting and integration with Slack/Jira; availability of attention and suitability insights.
- Legal/compliance features (exportable evidence logs, consent tracking, disclosure templates).
10) Quick-Start Templates You Can Adopt Today
Use these as starting points and tailor to your sector and risk tolerance.
Brand Safety & Suitability Policy (one-page per channel)
- Floor exclusions: violence, hate, illegal content, explicit sexual content.
- Conditional categories: political news (allow on quality publishers with ratings), user-generated content (only verified creators), live sports (CTV allowlist only).
- Creative rules: claims require citations; AI-generated visuals labeled when material; no synthetic endorsements without consent/disclosure.
Escalation Matrix (excerpt)
- S1: Legal/IP or privacy breach → Pause immediately; triage ≤2 hours; Legal + Marketing + Vendor.
- S2: Reputational harm (e.g., adjacency on borderline content) → Pause relevant lines; triage ≤1 business day.
- S3: Minor suitability (e.g., low-importance page category mismatch) → Optimize and document within 3 business days.
Pre-Bid Setup Checklist (DSP/Platform)
- [ ] Apply floor and custom suitability categories
- [ ] Enable fraud/SIVT filters
- [ ] Activate MFA/misinformation pre-bid segments
- [ ] CTV: app allowlist; SSAI checks
- [ ] RMN: prioritize O&O; validate attention metrics
AI Creative QA Checklist
- [ ] Disclosure required? Label applied
- [ ] Rights/consents logged; licenses stored
- [ ] Claims fact-checked; citations appended
- [ ] Model/version documented; guardrails validated
Post-Bid Monitoring Thresholds
- [ ] Brand risk alert if > latest global baseline (ref: ~1.5% in 2024)
- [ ] SIVT alert if >2% protected inventory
- [ ] Event surge protocol enabled (elections/crises)
Incident Response Snapshot
- [ ] Pause lines; capture evidence
- [ ] Notify stakeholders and vendors
- [ ] Legal triage (FTC/EU AI Act/GDPR)
- [ ] Root cause and recovery plan
What Not to Do (Hard-Learned Lessons)
- Don’t let a single “global blocklist” gather dust—risk evolves weekly; automate updates.
- Don’t skip post-bid validation on CTV—CTV fraud patterns and SSAI spoofing make this non-negotiable, as emphasized in 2024–2025 vendor insights (DV CTV bot patterns, 2024–2025).
- Don’t rely on disclosure “sometimes.” Build it into your publishing and creative workflows, consistent with EU/FTC expectations (FTC online advertising guidance; EU AI transparency pathways, 2025).
Bottom Line
In 2025, brand safety for AI media is a continuous discipline—not a setting you switch on. The teams that perform best combine clear governance, rigorous pre-bid controls, disciplined AI production practices, and post-bid monitoring with fast incident response. Back that with training, quarterly red-teaming, and a toolbox that fits your channels and legal obligations, and you’ll protect budgets while unlocking better performance.
Credits and review note: This playbook reflects hands-on deployments across agency and platform environments and aligns practices to 2024–2025 guidance from regulators and verification vendors cited above. If your programs span multiple jurisdictions, coordinate with counsel to localize obligations (EU AI Act timelines, FTC rules, and data protection requirements).
