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Cyren GlobalView telemetry inside a Vaikora policy: blocking AI agents that act on email-borne signals

Product Updates · Nov 11, 2026 · 4 min read

Consider this scenario: an AI agent reads an incoming message, extracts a URL, calls an external API to fetch content from that URL, and writes the result into a shared data store. Each of those steps is a tool call. None of them are inherently wrong. The question is whether the URL the agent is acting on has already been flagged as malicious infrastructure, and whether anything checks that before the fetch executes.

This is the gap that combining Cyren threat intelligence with Vaikora runtime enforcement is designed to close.

What Cyren GlobalView sees

Cyren's GlobalView telemetry network processes over a billion email, URL, and file transactions a day. That volume gives it visibility into phishing infrastructure during the staging period, before campaigns become active. When a domain is registered, configured, and seeded with credential-harvesting pages but not yet broadly distributed, Cyren's detection pipeline has already seen enough signal to classify it.

That classification feeds into indicators that security teams can pull into their SIEM, EDR, or enforcement layer. Cyren pushes indicators into CrowdStrike Falcon and SentinelOne. It ships as a packaged solution in the Microsoft Sentinel Content Hub, including the CCF connector, analytic rules, hunting queries, and workbooks.

The intelligence is real-time. It reflects what the network is seeing now, not what was manually curated last week.

Where most pipelines stop

Threat intelligence that reaches a SIEM or EDR is valuable for detection and investigation. It tells an analyst that a connection was made to a known-malicious host, or that a file hash matched a known bad indicator. That's useful after the fact.

It does not stop an AI agent that is currently mid-execution from completing the action. If an agent reads a phishing email, extracts the embedded URL, and calls a web-fetch tool, the fetch either happens or it doesn't. A SIEM alert fires after the HTTP request leaves the network.

The enforcement decision has to happen before execution, not as a consequence of it.

What Vaikora adds at runtime

Vaikora sits inline between an AI agent and the tools or LLM calls it makes. Before each action executes, Vaikora evaluates it against deterministic policy and returns one of four decisions: allow, block, modify, or require approval. That decision runs at P50 8ms and P99 45ms, with throughput exceeding 10,000 actions per second.

Policy can reference external threat intelligence. If a Cyren-flagged indicator, say a domain classified as active phishing infrastructure, appears in an agent's pending tool call parameters, Vaikora's policy can match on that value and return a block before the tool call fires. The agent never makes the fetch. The credential-harvesting page is never reached.

Every decision Vaikora makes is written to a SHA-256 hash-chained, tamper-evident audit log. The block is recorded with the input that triggered it, the policy rule that matched, and the timestamp. That record is auditable and cannot be retroactively altered.

A concrete worked example

An AI agent is processing an internal ticketing workflow. A ticket arrives containing a URL submitted by an external user. The agent's next step is to call a `fetch_url` tool to retrieve metadata from that URL and populate a knowledge base entry.

Vaikora intercepts the `fetch_url` call. The URL's domain is in the current Cyren indicator feed, classified as phishing infrastructure detected during staging. The policy rule evaluates the domain against the feed, matches, and returns a block decision. The tool call does not execute. The agent receives a policy rejection response and logs it. The audit chain records the event.

The analyst reviewing the next morning's logs sees the block, the matched indicator, the source URL, and the agent session that generated the call. No fetch, no data written to the knowledge base, full auditability.

Putting the two together

The practical integration path works in layers. Cyren's GlobalView detects and classifies malicious infrastructure early, feeding indicators into the platforms your team already uses. Vaikora consumes threat intelligence as a policy input and enforces decisions before each agent action executes. The combination means that intelligence generated from a billion daily transactions can influence runtime behavior inside an AI agent workflow, not just alert queues.

Vaikora integrates natively with Microsoft Sentinel, SentinelOne, CrowdStrike Falcon, Microsoft Defender, and AWS Security Hub. Cyren's indicators flow into the same platforms. If your environment already pulls Cyren data into Sentinel or Falcon, that signal is already close to where Vaikora policy enforcement runs.

What to check next

Start by auditing what your AI agents are allowed to do with URLs, domains, or external API endpoints they receive as input. Identify whether any threat intelligence currently reaching your SIEM or EDR is also being evaluated before those agent tool calls execute. If the answer is no, that is the gap to close first.

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