HUMAN Security this week expanded its Agentic Visibility capabilities beyond cybersecurity and fraud teams to serve marketing and commerce organizations. The announcement, made on April 21, 2026, also confirmed that the company is delivering these insights natively inside Adobe Experience Platform as an official Adobe technology partner.

The move reflects a growing gap between what AI traffic is doing on the web and what most marketing analytics tools are built to measure. According to HUMAN Security's 2026 State of AI Report, automation is now growing eight times faster than human traffic. Most analytics platforms were designed for a human-centric internet and offer no structured view into how AI crawlers, search agents, or agentic commerce flows interact with a brand's digital assets.

What Agentic Visibility does

The expanded capabilities are delivered through HUMAN's existing Sightline Cyberfraud Defense platform. Originally built to give security teams granular visibility into complex digital interactions, Sightline is now extended to marketing, commerce, and digital experience functions. The system identifies agent activity, classifies agents by type and trust level, and distinguishes between human and agentic traffic across the full customer journey.

Specifically, according to the announcement, marketing and commerce teams can now use Sightline to measure AI-driven traffic and engagement across websites, differentiate between human, bot, and AI agent activity with high precision, and understand how AI systems access, interpret, and interact with digital content. Teams can also develop policies and controls to manage AI access - whether to allow, limit, block, or redirect that activity. Additional capabilities include optimization for AI-driven discovery and engagement, covering strategies such as LLM optimization, answer engine optimization (AEO), and emerging agentic commerce patterns. Visibility into agent-driven activity extends to key workflows including product discovery, checkout, and loyalty programs.

This is not a narrow security product being rebranded. The platform leverages deep behavioral, technical, and network intelligence to classify interactions, assess trust levels, and produce actionable insights about both human and non-human engagement. The distinction between a legitimate AI shopping agent acting on behalf of a consumer, a scraper harvesting pricing data, and a human browsing a product page is exactly the kind of classification that traditional analytics platforms were not built to make.

The numbers behind the announcement

The data that frames this expansion is substantial. According to HUMAN Security's 2026 State of AI Report, released on April 9, 2026, automation is growing eight times faster than human traffic. Separately, Adobe released data at its Adobe Summit event on April 20, 2026, showing that AI traffic to U.S. retail sites surged 269% year over year in March 2026 alone. Gartner projects that AI agents could drive 80% of internet traffic by 2035, which would make the current visibility gap not a future concern but an accelerating present one.

These figures do not appear in isolation. As PPC Land has documented, AI bot traffic to retail websites grew 5.4 times during 2025, according to Botify analysis published in a March 2026 report. OpenAI alone generates approximately one website visit for every 198 crawls - a ratio that stands in sharp contrast to Google's one visit per six crawls. That asymmetry matters enormously for marketing teams trying to understand traffic quality and attribution. During Super Bowl LX in February 2026, HUMAN Security tracked a 5% rise in AI agent activity alongside 74 million blocked retail scraping attacks, underscoring how quickly automated activity can concentrate around specific moments.

The problem is compounded by the fact that traditional analytics tools surface almost none of this. When an AI agent crawls a product catalogue, evaluates pricing, or interacts with a checkout flow, those interactions may appear in server logs but offer little structured signal inside standard marketing dashboards. The conversion path gets obscured. Attribution models built around human browsing sessions do not map cleanly onto agentic behavior, which can involve dozens of interactions before any human ever touches the purchase.

The Adobe integration

The decision to embed Agentic Visibility inside Adobe Experience Platform is significant on its own. Adobe is among the dominant infrastructure providers for enterprise marketing, analytics, and customer data management. Delivering this intelligence as a native part of how marketing teams already measure performance - rather than as a separate security tool requiring a separate login and workflow - removes a structural barrier to adoption.

Adobe announced the general availability of its Experience Platform Agent Orchestrator in September 2025, establishing a framework for managing agents from Adobe and third-party ecosystems within its platform. HUMAN's integration slots into that architecture, bringing bot and agent classification data into the same environment where marketers already analyze customer journeys. As PPC Land has tracked, the broader ad tech industry's infrastructure push around AI agentsaccelerated sharply in late 2025, with Amazon, Google, and the IAB Tech Lab each releasing agent-related tools within the same week in November.

The HUMAN-Adobe partnership extends that infrastructure push into the measurement layer. Organizations that already use Adobe Experience Platform for audience management, journey orchestration, or performance analytics can now see AI-driven traffic alongside their existing human-traffic data, within the same interface.

Agentic Trust as an emerging discipline

HUMAN Security describes itself as the global leader in Agentic Trust - a term the company uses to describe the emerging discipline that informs and governs how humans, bots, and AI agents operate online. The concept sits at the intersection of cybersecurity, data governance, and marketing operations. The company has specialized in understanding and mitigating automated traffic risk for more than a decade, protecting what it describes as the world's largest brands, advertising platforms, and commerce networks.

According to John Searby, Chief Strategy Officer at HUMAN Security, "Marketing and commerce teams are being asked to adapt to a fundamental shift in how customers discover and interact with brands, but they lack the tools to see what's actually happening. Sightline changes that. It gives teams a clear view of AI-driven activity so they can develop their policies, implement controls, and fully participate in the opportunities created by AI."

The framing here is deliberate. The company is not positioning this as a defensive product. The emphasis on "opportunities created by AI" signals that Agentic Visibility is being offered as a growth tool as much as a risk management one. Marketing teams that can see where AI agents are engaging with their brand assets - which products are being discovered, how AI systems represent pricing, which content formats AI crawlers can actually parse - gain information they can act on commercially.

Why this matters for marketing measurement

The underlying technical problem is not trivial. AI agents do not behave like human browsers. They may not execute JavaScript. They often access structured data endpoints rather than rendered pages. Their interaction patterns differ from human sessions in timing, sequencing, and depth. When these interactions appear in analytics tools at all, they often appear as bot traffic to be filtered out - removing signal that could be commercially relevant.

Microsoft Clarity launched Bot Activity tracking in January 2026, giving website operators a view into which AI systems are crawling their properties. That move addressed the identification problem at the infrastructure level. HUMAN's Sightline goes further by classifying agents by type and trust level and connecting that classification to marketing workflows inside platforms like Adobe Experience Platform. The goal is not just to know that a bot visited - it is to understand what kind of agent it was, what it did, and what that means for engagement strategy.

The measurement challenge is especially acute in e-commerce. Product discovery, price comparison, and checkout initiation increasingly happen inside AI interfaces before a consumer ever visits a retailer's site. If a consumer uses an AI assistant to research a product, receive a recommendation, and then complete a purchase - the retailer's analytics may show only the final transaction, with no visibility into the earlier agentic activity that shaped the decision. HUMAN's expanded platform is designed to surface that earlier layer.

This connects to a broader set of emerging optimization disciplines. LLM optimization and answer engine optimization are developing as structured practices to make brand content more accessible and accurately represented inside AI systems. Those practices require understanding how AI agents currently interact with a brand's digital properties - which is precisely the data gap that Agentic Visibility targets.

Context from the industry

The timing of this announcement aligns with a concentrated period of agentic AI announcements across the marketing technology landscape. As documented by PPC Land's coverage of ad tech's AI agent push in April 2026, Adobe itself introduced AI agents for digital marketing and ad strategy automation on April 21, 2026, the same day as HUMAN's announcement. The Trade Desk launched Koa Agents the following day. Google announced a $750 million fund to support cloud partners in advancing agentic AI transformation on April 22.

The convergence of security intelligence with marketing measurement reflects a structural shift in how the internet functions. As PPC Land has reported, the agentic commerce infrastructure is expanding rapidly, with autonomous agents now capable of completing purchases, managing loyalty accounts, and conducting product research without human involvement at each step. Measurement tools built for human-only sessions cannot keep pace with this.

HUMAN Security's platform analyzes over a quadrillion digital interactions each year, according to the company. That scale of behavioral signal data is the foundation for the classification capabilities being extended to marketing teams today. The company powers its Sightline platform with what it describes as one of the world's largest behavioral signal networks, combining behavioral, technical, and network intelligence to distinguish legitimate activity from fraud, abuse, and automated manipulation.

Timeline

Summary

Who: HUMAN Security, a New York-based cybersecurity company specializing in bot mitigation and digital trust, announced the expansion. John Searby, Chief Strategy Officer, is the primary spokesperson.

What: HUMAN Security expanded its Agentic Visibility capabilities within the Sightline Cyberfraud Defense platform to serve marketing and commerce organizations, enabling them to measure, classify, and act on AI agent traffic across their digital properties. The company simultaneously confirmed an integration with Adobe Experience Platform as an official Adobe technology partner.

When: The announcement was made on April 21, 2026, at 9:00 AM ET. The 2026 State of AI Report underlying the expansion was released on April 9, 2026.

Where: The capabilities operate within HUMAN's Sightline Cyberfraud Defense platform and are delivered natively inside Adobe Experience Platform and other leading analytics tools. HUMAN Security is headquartered in New York.

Why: Automated internet traffic is growing eight times faster than human traffic, according to HUMAN's own data, while most analytics platforms remain designed for human-only web interactions. AI agents - including crawlers, search agents, and agentic commerce systems - are already reshaping traffic, engagement, and conversion patterns in ways that are largely invisible to standard marketing measurement tools. With Gartner projecting AI agents could account for 80% of internet traffic by 2035, the gap between what is happening on the web and what marketing teams can see represents a material risk to measurement accuracy and commercial decision-making.

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