Navigating Brand Safety and Suitability in the AI Era
The digital advertising landscape is being reshaped by rapid advancements in artificial intelligence (AI). As brands, agencies, verification firms, and publishers navigate an increasingly complex and content-heavy environment, AI-powered ad verification has become essential. It plays a critical role in protecting brand suitability, preventing ad fraud, and tackling the growing threat of synthetic content generated by generative AI (Gen AI).
This article examines how AI is transforming ad verification across three key stakeholder groups: verification companies, brands and agencies, and publishers. By harnessing advanced AI technologies like natural language processing (NLP), computer vision, and real-time analytics, the industry can ensure ads appear in safe, credible, and contextually appropriate environments—ultimately protecting brand reputation, maximizing media investment, and maintaining audience trust in an ever-evolving digital world.
From the Ad Verification Companies: Building Trust with AI
Leading ad verification firms like Integral Ad Science (IAS) and DoubleVerify (DV) are pioneering the use of AI to deliver advanced solutions for brand suitability and fraud prevention. These companies are not only tackling traditional ad fraud but also addressing emerging threats from generative AI (Gen AI), helping advertisers navigate a fast-evolving digital landscape with greater confidence.
Real-Time Protection at Scale
IAS processes over 280 billion interactions daily, analyzing the equivalent of 40 years of video content every day. Using machine learning, natural language processing (NLP), and computer vision, IAS delivers scalable and precise verification. Its multi-layered framework—supported by the IAS Threat Lab—combines pre-bid and post-bid controls to classify millions of URLs and conduct frame-by-frame video analysis. This ensures ads appear in safe, relevant environments, free from harmful or fraudulent content (IAS, 2024).
Similarly, DV’s Universal Content Intelligence™ engine takes a holistic approach, integrating computer vision, optical character recognition (OCR), and NLP to analyze visual, audio, and textual elements across web, mobile, CTV, and social platforms. Its fraud detection models—refined over 15 years—assign risk scores to identify Gen AI-driven threats such as fake reviews and synthetic publisher identities. DV’s Scibids AI™ solution further enhances campaign performance by converting data signals into intelligent bidding strategies, reducing manual optimization and scaling results across thousands of campaigns (DoubleVerify, 2025).
Combatting Gen AI-Driven Threats
Both IAS and DV are equipped to detect Gen AI-generated content, including “slop sites”—low-quality, ad-heavy domains designed to exploit programmatic ad spend. IAS uses models that analyze text, visual design, and behavioral patterns to flag Made-for-Advertising (MFA) sites, while DV’s Fraud Lab monitors threats like cloned publisher sites and falsified user agents. By analyzing patterns in known fraud schemes, their models learn what fraud looks like, apply learnings across traffic and assign risk scores across environments. This enables faster detection, investigation and mitigation of fraud threats. These capabilities ensure ad budgets are directed toward legitimate, high-quality inventory, protecting both campaign efficiency and brand equity (IAS, 2024; DoubleVerify, 2025).
📌 *Learn more about MFA sites and their impact on advertisers: FraudBlocker article*
Looking Ahead: Key Trends in Ad Verification
With projections suggesting that up to 90% of online content could be AI-generated in the near future (IAS, 2024), verification companies are focusing on several critical trends:
- Advanced Detection Models: Tackling Gen AI-driven “slop” and deepfakes through metadata analysis, linguistic pattern recognition, and source credibility checks.
- Global Scalability: Adapting solutions across fragmented media channels and diverse regions, while accounting for local cultural and regulatory nuances.
- Attention-Based Metrics: Moving beyond traditional viewability to metrics like Attentive CPMs, which better reflect genuine user engagement.
- AI-Driven Efficiency: Automating programmatic bidding and reducing manual tasks to free up teams for strategic decision-making.
From the Brands and Agencies: Strengthening Reputation and Minimizing Media Wastage
Why Does Brand Safety and Suitability Matters?
The global digital ecosystem—spanning mobile apps, social media, and connected TV (CTV)—poses significant risks. Ads appearing next to harmful content such as misinformation, hate speech, or inappropriate material can severely damage brand perception; in DV’s recent Global Insights Report: APAC, nearly half of surveyed consumers revealed they are “less likely to purchase from brands whose ads appear alongside objectionable content” (DV Global Insights: APAC, 2025).
Ad fraud is another persistent challenge, with global losses projected to reach $100 billion by 2025 (Juniper Research, 2023). Consumer trust is equally critical—65% of APAC consumers are less likely to trust brands associated with low-quality or misleading content (Kantar, 2024).
The Value of AI-Powered Verification
AI is transforming how brands and agencies protect and optimize their advertising efforts. Through technologies like natural language processing (NLP) and sentiment analysis, AI ensures ads are placed in culturally appropriate and brand-safe environments, reducing reputational risk. In APAC’s linguistically and culturally diverse markets, AI can differentiate between content that aligns with brand values and material that may conflict with local norms or regulations.
Real-time anomaly detection—such as bot-driven clicks— helps identify and block fraudulent activities, ensuring ad budgets are spent on genuine impressions. Additionally, AI-driven contextual targeting improves campaign performance by aligning ads with audience interests, leading to up to 30% higher click-through rates in APAC (IAB Southeast Asia, 2023). These capabilities contribute to stronger engagement, better ROI, and sustained consumer trust.
Best Practices for Implementation
To fully leverage AI in ad verification, brands and agencies should consider adopting the following strategies:
Integrate Advanced AI Verification Tools
To effectively safeguard brand reputation and optimize media spend, brands and agencies should collaborate with trusted ad verification providers. Integral Ad Science (IAS) and DoubleVerify (DV) have developed advanced platforms that use technologies such as machine learning, natural language processing (NLP), and computer vision to analyze content across multiple formats and languages. These tools help ensure ads are placed in safe, culturally appropriate environments, reducing the risk of reputational damage and wasted ad spend.
Define Custom Brand Suitability Guidelines
While brand suitability focuses on keeping ads away from harmful or inappropriate content, brand suitability goes a step further—it ensures ads appear in environments that are not just safe, but also aligned with a brand’s values, tone, and audience expectations.
A one-size-fits-all approach to ad verification can lead to over-blocking—where ads are unnecessarily restricted from appearing on content that is actually suitable and valuable. This can limit reach, reduce campaign efficiency, and waste media spend.
By defining custom brand suitability guidelines, marketers can:
- Tailor content labels to reflect your brand’s unique positioning and risk tolerance.
- Avoid over-blocking, which helps maintain scale and reach without compromising brand equity.
- Ensure cultural relevance, especially in multilingual markets where context and sentiment can vary significantly.
Leverage Real-Time Monitoring
In a digital environment that evolves by the minute, especially across a fragmented media landscape, brands and agencies need real-time visibility into where their ads are appearing—and the ability to act immediately when issues arise.
AI-powered verification tools enable continuous monitoring of ad placements, detecting unsafe or unsuitable content as it emerges. This allows marketers to respond instantly, preventing reputational damage and minimizing wasted spend.
Address Consumer Mistrust in AI-Generated Ads
With 60% of APAC consumers expressing concern about misleading AI-generated content (Kantar, 2024), brands should use clear disclosures and prioritize high-quality visuals to maintain transparency and trust.
How Publishers Can Maintain Audience Trust in a New Digital Age
AI-Driven Brand Suitability and Ad Verification: Transforming Trust in Digital Advertising
In today’s digital ecosystem, brand safety and content authenticity are critical for publishers aiming to maintain audience trust and attract premium advertisers. With 60% of U.S. adults prioritizing a brand’s values when making purchase decisions, ensuring ad environments align with these expectations is paramount. Trusted news publishers, such as the South China Morning Post (SCMP), who leverage advanced AI-driven tools like SCMP Signal, are setting new standards for brand suitability, addressing both traditional ad fraud and the emerging threat of generative AI-produced fake content. Through contextual targeting and real-time adaptability, these tools create safe, relevant ad inventories that enhance campaign performance while safeguarding brand integrity.
The Imperative of Brand Suitability and Credibility
Brand suitability extends beyond avoiding harmful content—it’s about fostering trust with both audiences and advertisers. Ad placements near topics that do not align with a brand’s values can erode consumer trust; therefore,rusted news publishers like SCMP, known for editorial rigor, offer premium, verified environments that align with advertisers’ values. This approach not only protects reputations but also drives engagement, delivering higher campaign ROI. By providing reliable, contextually relevant ad placements, these publishers mitigate the risks of low-quality or fraudulent content, including the growing challenge of generative AI creating misleading or fabricated articles.
AI-Powered Contextual Targeting
Advanced AI tools, such as SCMP Signal, are redefining brand suitability by evolving it into brand suitability through sophisticated contextual analysis. Unlike traditional keyword blocklists, which may flag safe content—like the word “shot” in a sports article due to its association with violence—modern AI leverages natural language processing (NLP) and sentiment analysis, such as the Valence Aware Dictionary for Sentiment Reasoning (VADER). These tools analyze sentiment, keywords, and content holistically, ensuring ads align with the tone and intent of articles.
For instance, an article mentioning “shot” in a sports context, like a football match, can be identified as suitable for brands, distinguishing it from content about violence with similar keywords. This nuanced approach prevents overblocking, preserving premium ad inventory. Early data from a 2020 health and fitness campaign showed a 35% performance boost when sentiment targeting was applied, highlighting the value of AI-driven precision in enhancing engagement and campaign outcomes.
Combating Traditional and Gen AI Fraud
AI tools like SCMP Signal also address ad fraud, including click fraud and impression manipulation, by verifying traffic and ensuring ad placements occur in legitimate environments. The rise of generative AI introduces new challenges, such as fabricated content mimicking credible sources. Advanced AI systems counter this by cross-referencing metadata, linguistic patterns, and source credibility to detect and flag synthetic content. This ensures advertisers’ campaigns run in authentic, high-quality environments, maintaining trust and effectiveness.
Real-Time Adaptability to Dynamic Challenges
AI’s ability to learn in real-time is crucial for navigating volatile news cycles. During global events like pandemics or conflicts, AI dynamically adjusts its analysis, flagging or reclassifying content as contexts shift. For example, during the COVID-19 outbreak, while basic blocklists flagged 67% of pages with COVID-related keywords as unsafe, advanced contextual tools identified 58.5% of similar pages as safe, preserving valuable inventory. This adaptability ensures advertisers avoid harmful adjacencies while capitalizing on relevant, high-quality content.
The Role of Trusted Publishers
Partnering with trusted news publishers is essential for premium advertisers. Unlike unverified platforms that risk associating brands with fake or low-quality content, established publishers offer editorial integrity and AI-driven precision through tools. By combining sentiment analysis, keyword tagging, and readability insights, these platforms create safe, scalable ad environments. Campaigns in such contexts have shown up to 40% performance increases. As privacy regulations tighten and third-party cookies decline, contextual targeting via trusted publishers becomes a strategic necessity, delivering privacy-safe, impactful ad placements.
In an era where trust is paramount, AI-driven tools like SCMP Signal empower advertisers to navigate the complexities of brand suitability and ad verification. By leveraging contextual targeting, sentiment analysis, and real-time adaptability, these solutions address traditional ad fraud and the growing threat of generative AI content. Trusted publishers equipped with these technologies provide advertisers with confidence, ensuring campaigns resonate with audiences while safeguarding brand reputations in a dynamic digital landscape.
Conclusion
AI-powered ad verification is reshaping the digital advertising landscape by addressing traditional ad fraud and the emerging threat of Gen AI-generated content. Verification companies like IAS and DoubleVerify leverage advanced AI to deliver real-time, contextually precise solutions, ensuring safe and fraud-free ad placements. Brands and agencies benefit from optimized ad spend, enhanced campaign performance, and sustained consumer trust. Trusted publishers, equipped with tools like SCMP Signal, create premium ad environments that drive engagement and align with brand values. As the digital ecosystem grows more complex, AI’s scalability, adaptability, and precision—already robust and continuously evolving—empower stakeholders to navigate challenges with confidence, ensuring advertisements resonate in credible, high-impact settings.
Reference
- DV Global. (2025). DV Global Insights: APAC, 2025. Available at: https://doubleverify.com/2025-dv-global-insights-apac-report/
- Juniper Research. (2023). *Global Digital Advertising Fraud Report*. Estimate of $100 billion annual ad fraud loss by 2025. Available at: https://www.juniperresearch.com
- Kantar. (2024). *APAC Consumer Insights Report*. Study on consumer trust and perceptions of ads in APAC markets. Available at: https://www.kantar.com
- IAB Southeast Asia. (2023). *Programmatic Advertising Guidelines*. Report on contextual targeting effectiveness in APAC. Available at: https://www.iab.com/guidelines/programmatic-advertising-guidelines-2023
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Louis Ng |  |
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| Timmy Bankole IAB Hong Kong & AdTech and Programmatic Committee Member |  |
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| Article launched on 25/9/2025 |
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APPENDIX
About the IAB (Hong Kong)
The Interactive Advertising Bureau (Hong Kong) empowers the media and marketing industries in Hong Kong, to thrive in the digital economy. It is comprised of more than 100 leading media and technology companies that are responsible for selling, delivering, and optimizing digital campaigns. Working with its member companies, the IAB (Hong Kong) evaluates and recommends standards and practices and fields critical research on interactive advertising.
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