March 2026 Summaries

Digital publishers are grappling with the challenge of managing AI traffic, as large language models (LLMs) and AI crawlers can either be a new source of revenue or a potential threat to existing business models. Some media organizations have begun monetizing AI access by licensing content to compliant LLMs, partnering with monetization platforms, and negotiating access deals. However, without accurate AI and bot detection, these strategies risk being undermined by unauthorized access and content scraping, leading to potential revenue loss and degraded SEO. Effective detection is crucial to differentiate between legitimate AI interactions that can generate business value and malicious activities that can erode it. As AI traffic grows, media companies must implement robust detection systems to enforce licensing agreements, attribute revenue accurately, and maintain their competitive edge, as demonstrated by Mansueto Ventures, which successfully monetized AI traffic through partnerships and advanced detection technologies.

As AI systems like ChatGPT and Claude increasingly perform online tasks such as account registration and transactions on behalf of users, a new threat known as agentic fraud has emerged, requiring enhanced security measures. This fraud exploits AI's ability to mimic legitimate user behavior, posing challenges for distinguishing between benign and malicious AI agent activity. Forrester has rebranded its Bot Management to Bot & Agent Trust Management, reflecting the need for specialized controls to manage this new category of traffic. By 2030, agentic commerce could significantly boost global retail revenues, but its rise amplifies risks like credential abuse and session hijacking. Security teams face the challenge of implementing a framework that assesses agent trust through identity, intent, behavior, and authorization analysis, shifting from binary blocking to contextual trust assessments. Companies like DataDome offer solutions to detect and block fraudulent agent activity in real-time, ensuring legitimate transactions proceed smoothly while protecting against the evolving landscape of agentic fraud.

As agentic AI continues to evolve, traditional identity-based security measures are proving insufficient, requiring a shift towards a dual-layered approach that incorporates both identity verification and intent detection to enhance cybersecurity and fraud prevention. While identity verification methods, such as cryptographic token verification and trusted IP ranges, confirm the authenticity of AI agents, they fall short in discerning the intent behind their actions, which is crucial for identifying potential threats like fraud or data exfiltration. DataDome exemplifies this multi-layered detection strategy by employing dynamic Trust Scores that evaluate both the identification strength and behavioral intent of AI agents, aligning with specific business objectives and continuously adapting through feedback loops. This approach transforms fraud prevention from a mere security measure into a strategic business enabler, allowing companies to manage agentic traffic as an asset rather than a risk by distinguishing between valuable and harmful interactions. By integrating real-time intent analysis with existing business systems, organizations can make informed decisions that align with their long-term objectives, ultimately driving revenue and enhancing customer experiences in an increasingly AI-driven landscape.

Gift cards offer numerous benefits to businesses by increasing store traffic, enhancing loyalty programs, and satisfying customers, while also providing gift-givers and recipients with convenience and flexibility. However, they are increasingly targeted by cybercriminals, making gift card fraud a significant issue; the Federal Trade Commission reported $212 million in losses from scams involving gift cards and prepaid cards in 2024. The global gift card market, valued at $1.29 trillion in 2024 and projected to reach $5.22 trillion by 2034, presents a lucrative target for attackers who employ automated bot networks for fraudulent activities such as card cracking and laundering stolen credit card funds. These AI-driven attacks not only lead to financial losses and chargebacks but also degrade website performance and strain merchant infrastructure. To combat these issues, businesses need to implement comprehensive cyberfraud protection measures that can detect and block malicious bot traffic while ensuring a seamless experience for legitimate customers.