Today’s security ecosystem generates a staggering amount of complex telemetry. For instance, processing a single email requires analyzing sender reputation, authentication results, link behavior, infrastructure metadata, and countless other attributes. Simultaneously, Cloud access security broker (CASB) engines continuously scan SaaS environments for signals that detect misconfigurations, risky access, and exposed data.

But while detections have become more sophisticated, explanations have not always kept pace.

Security and IT teams are often aware when something is flagged, but they do not always know, at a glance, why. End users are asked to make real-time decisions about emails that may impact the entire organization, yet they are rarely given clear, contextual guidance in the moment that matters.

Cloudy changes that.

Cloudy is our LLM-powered explanation layer, built directly into Cloudflare One. It translates complex machine learning outputs into precise, human-readable guidance for security teams and end users alike. Instead of exposing raw technical signals, Cloudy surfaces the reasoning behind a detection in a way that drives informed action.

For Cloudflare Email Security, this means helping users understand why a message was flagged before they escalate it to the security operations center, or SOC. For Cloudflare CASB, it means helping administrators quickly understand the risk and remediation path for SaaS findings without having to manually assess low-level signals.

This post outlines how we are extending Cloudy across Phishnet and API CASB to improve decision making, reduce unnecessary noise, and turn complex security signals into clear, actionable insight.

When an email is analyzed by Cloudflare Email Security, it is not evaluated by a single signal or model. Instead, a wide range of machine learning models analyze different parts of the message, from sender reputation and message structure to content, links, and behavioral patterns. This model set continues to grow as our machine learning team regularly trains and deploys new detections to keep pace with evolving threats.

Based on this analysis, messages are labeled with outcomes such as Malicious, Suspicious, Spam, Bulk, or Spoof. While these detections have been effective, we consistently heard feedback from customers that it was not always clear why a message was flagged. The decision was correct, they told us —  but the reasoning behind it was often opaque to both end users and security teams.

To address this, we introduced the first version of Cloudy: LLM-powered summaries for detections. These summaries translate what our machine learning models are seeing into human readable explanations. Initially, these summaries were available in the Cloudflare dashboard to help SOC teams during investigations. Over the past few months, customer feedback has confirmed that these explanations significantly improve understanding in our detections.

As we continued speaking with customers, another challenge surfaced. Our Phishnet tool allows users to submit messages to the SOC when they believe an email may be suspicious. While this empowers employees to participate in security, many SOC teams told us their queues were being flooded with submissions that turned out to be clean messages.

The result was unnecessary backlog and slower response times for emails that actually required investigation.

At the same time, customers told us that traditional security awareness training was not always enough. Users still struggled to evaluate emails in the moment, when it mattered most. They wanted more contextual guidance directly within the workflow where decisions are made.

This upgrade is designed to address both of these problems. By bringing clearer explanations and contextual education directly into Phishnet, we aim to help users make better decisions while reducing noise for SOC teams, without sacrificing security.

As organizations and attack techniques have evolved, so has the role of the end user. Modern email threats increasingly rely on social engineering, subtle impersonation, and psychological pressure which places users directly in the decision path.

In response, users are being asked to act as an additional layer of defense. However, traditional security awareness tools often fall short. Training is typically delivered through periodic sessions or simulated phishing campaigns, disconnected from real messages and real decisions. When users encounter an unfamiliar email, they are left without enough context to confidently assess risk.

This gap commonly leads to one of two outcomes. Some users submit nearly every questionable message to the SOC, creating excessive noise and slowing down investigations. Others interact with messages they should not, simply because nothing in the moment signals clear risk.

By embedding Cloudy directly into Phishnet, we close this gap. 

Users receive immediate, contextual explanations that help them understand what Cloudflare is seeing and why a message may be risky. This enables users to make informed decisions at the point of interaction, reduces unnecessary escalations to the SOC, and allows security teams to focus on the messages that truly require attention.

Over time, this approach shifts users from being a source of noise to becoming an effective part of the detection and response workflow. The result: stronger email security, without adding friction or burden to security teams.

In the next month, we will be upgrading our Phishnet reporting button to extend the Cloudy summaries.

_{The new Phishnet screens will show Cloudy summaries.}

With this upgrade, end users receive a simplified, user-friendly version of Cloudy summaries at the moment they report a message. These summaries are generated in real time using Cloudflare Workers AI and run directly on Cloudflare’s global Workers platform when a user interacts with a message in Phishnet.

When a user clicks the Phishnet reporting button, the request triggers a Workers-based workflow that aggregates structured outputs from multiple detection models associated with that message. These model outputs include signals such as sender reputation, domain and infrastructure characteristics, authentication results, link and content analysis, and behavioral indicators collected during message processing.

The aggregated signals are then passed to Workers AI, where a series of purpose-built prompts generate a natural language explanation. Each prompt is designed to transform low-level detection outputs into a concise and human-readable summary. This process focuses on explanation rather than classification and does not alter the original disposition of the message.

_{How Cloudy transforms detections into clear explanations.}

For this experience, we intentionally redesigned the summaries compared to those shown to administrators in the Cloudflare dashboard. During testing, we found that admin-focused summaries often relied on technical concepts that were difficult for non-technical users to interpret. Terms such as ASNs, IP reputation, or authentication failures required translation. 

To ensure end users can understand the summaries, Phishnet emphasizes plain-language explanations while preserving the meaning of the underlying detections.

Signal	What it means	Cloudy translation for end users
SPF Fail	Sender explicitly not authorized by SPF	This email failed a sender verification check.
DKIM Fail	Message signature does not validate	The message integrity check failed, which can be a sign of tampering.
DMARC Fail	DMARC policy check failed	The sender’s domain could not confirm this email is legitimate.
Reply to Mismatch	Reply To differs from From	Replies may go to a different address than the sender shown.
Domain Age	Domain recently registered	The sender domain is newly created, which is common in phishing.
URL Low Reputation	Destination URL has poor reputation	The link destination has signals associated with risk.

Because this workflow runs on the Cloudflare Workers platform, summaries are generated with low latency and at global scale — so users receive immediate feedback at the moment of interaction. This real-time context allows users to better understand why an email may be risky or why it appears safe before deciding whether to escalate it to the SOC.

We are currently beta testing this experience with Microsoft customers to ensure the summaries are accurate and reliable. Cloudy summaries are not trained on customer data. We are also applying additional validation to ensure the generated explanations do not hallucinate. Accuracy is critical at this stage as incorrect guidance could introduce real security risk.

Following the beta period, we plan to expand access to all Microsoft users. We will also bring similar upgrades to the Phishnet sidebar for Google Workspace users later in 2026.

But helping end users better understand what makes an email risky is only part of the story. We are also applying Cloudy to the administrative side of security operations, where clarity and speed matter just as much. Beyond Phishnet, Cloudy now translates complex CASB findings into structured explanations that help security and IT teams quickly understand risk, prioritize remediation, and take confident action across their SaaS environments.

Inside Cloudflare One, our SASE platform, CASB connects to the SaaS and cloud tools your teams already use. By talking to providers over API, CASB gives security and IT teams:

• A consolidated view of misconfigurations, overshared files, and risky access patterns across apps like Microsoft 365, Google Workspace, Slack, Salesforce, Box, GitHub, Jira, and Confluence (CASB Integrations).

• Continuous scanning for new issues as users collaborate, share, and adopt new tools.

• Findings that are organized, searchable, and exportable for triage and reporting.

_{A typical CASB Findings page showing detections for a Microsoft 365 finding.}

Until now, understanding what exactly triggered a CASB Finding — the detections that CASB makes across connected SaaS integrations — has been a black box. While the information was there to put together an explanation of why that file, that user, that configuration was triggering a CASB Finding Type, it wasn’t exactly obvious the reason why it was ultimately detected by our system.

With the introduction of Cloudy summaries in CASB, users receive a short description of the detection rationale with the specific details of the match listed out for easy comprehension.

Unlike a simple text summary, Cloudy for CASB provides a structured breakdown designed for immediate remediation. As seen in our beta testing across different providers, from Microsoft 365 to Dropbox, the model consistently parses findings into two distinct sections:

• Risk: It identifies exactly why the finding matters. For instance, rather than just noting a 'Suspended User,' Cloudy clarifies that this 'may indicate a compromised account or a user who should no longer have access to company data'.

• Guidance: It offers immediate next steps. Instead of generic advice, it suggests specific actions, such as verifying if a suspension was intentional or reviewing an application's legitimacy before revoking access.

This structure ensures that analysts can understand the gravity of a finding without needing deep expertise in the specific SaaS application involved.

_{An example Cloudy Summary in a CASB Posture Finding.}

Finding Type	Technical Signal	Cloudy Translation (Risk & Guidance)
Identity & Access	Dropbox: Suspended User	Risk: A suspended user account may indicate a compromised account or a user who should no longer have access to company data. Guidance: Verify that the suspension is intentional and that the user's access has been properly revoked.
Shadow IT	Google Workspace: Installed 3rd-party app	Risk: This installed application with Google Sign In access may pose a risk of unauthorized access to user data. Guidance: Review the application's legitimacy and necessity, and consider revoking access if it is no longer needed.
Email Security	Microsoft 365: Domain DMARC record not present	Risk: The absence of a DMARC record may leave the domain vulnerable to email spoofing and phishing attacks. Guidance: Configure a DMARC record for the domain to specify how to handle unauthenticated emails.
Data Loss Prevention	Microsoft 365: File publicly accessible + DLP Match	Risk: This file being shared publicly with edit access may allow unauthorized modifications... especially given the potential sensitive content indicated by the DLP Profile match. Guidance: Review the file's content... and consider restricting access if necessary.

We know that when it comes to our customers getting to the bottom of identified security issues, time is of the essence. We believe that any amount of unnecessary uncertainty or lack of clarity around what’s going wrong just puts more time between an imperfect state and one that is more secure.

We built this feature on the same privacy-first foundations as all products at Cloudflare. Cloudy summaries in CASB are generated using Cloudflare Workers AI, ensuring that your data remains within our secure infrastructure during analysis. The models are not trained on your SaaS data, and the summaries are generated ephemerally to aid in triage. This allows your team to leverage the speed of AI without exposing sensitive internal documents or configurations to public models.

For Email Security, we will continue to expand how Cloudy supports both administrators and end users. Our focus is on delivering clearer explanations, better in context guidance, and deeper integration into daily workflows.

For CASB, we’re excited to look for opportunities where Cloudy can make it even easier for CASB administrators to understand what’s going on across their cloud and SaaS apps. Keep an eye out as we look to expand Cloudy coverage to allow administrators to query their findings using natural language, further reducing the time it takes to identify and remediate risks.

Looking ahead, this includes richer explanations for additional detection types, tighter feedback loops between user actions and detections, and continued improvements to how users and SOC teams collaborate through Phishnet. Our goal is to make Cloudy a core part of how organizations understand, trust, and act on email security decisions.

We provide all organizations (whether a Cloudflare customer or not) with free access to our Retro Scan tool, allowing them to use our predictive AI models to scan existing inbox messages in Microsoft 365. 

Retro Scan will detect and highlight any threats found, enabling organizations to remediate them directly in their email accounts. With these insights, organizations can implement further controls, either using Cloudflare Email Security or their preferred solution, to prevent similar threats from reaching their inboxes in the future.

If you are interested in how Cloudflare can help secure your inboxes, sign up for a phishing risk assessment here.

Email security has always been defined by impermanence. It is a perpetual call-and-response arms race, where defenses are only as strong as the last bypass discovered and attackers iterate relentlessly for even marginal gains. Every control we deploy eventually becomes yesterday’s solution.

What makes this challenge especially difficult is that our biggest weaknesses are, by definition, invisible.

This problem is best illustrated by a classic example from World War II. Mathematician Abraham Wald was tasked with helping Allied engineers decide where to reinforce bomber aircraft. Engineers initially focused on the bullet holes visible on planes returning from missions. Wald pointed out the flaw: they were reinforcing the areas where planes could already take damage and survive. The true vulnerabilities were on the planes that never came back.

Email security faces an identical hurdle: our detection gaps are unseen. By integrating LLMs, we advance email phishing protection and move from reactive to proactive detection improvement.

The limits of reactive defense

Traditional email security systems improve primarily through user-reported misses. For example, if we marked a spam message as clean, customers can send us the original EML to our pipelines for our analysts to analyze and update our models. This feedback loop is necessary and valuable, but it is inherently reactive. It depends on someone noticing a failure after the fact and taking the time to report it.

That means detection improvements are often driven by what attackers already succeeded at, rather than by what they are about to exploit next.

To close this gap, we need a way to systematically observe the “planes that didn’t make it back.”

Large Language Models (LLMs) hit the mainstream market in late 2022 and early 2023, fundamentally changing how we process unstructured data. At their core, LLMs use deep learning and massive datasets to predict the next token in a sequence, allowing them to understand context and nuance. They are particularly well-suited for email security because they can read natural language and characterize complex concepts (like intent, urgency, and deception) across millions of messages.

Every day, Cloudflare processes millions of unwanted emails. Historically, it was not feasible to deeply characterize each message beyond coarse classifications. Manually mapping emails to nuanced threat vectors simply did not scale. 

Now, Cloudflare has integrated LLMs into our email security tools to identify threats before they strike. By using the power of LLMs, as we’ll describe below, we can finally see a clear and comprehensive picture of the evolving threat landscape.

^{Our LLM-driven categorization shows clear spikes and persistent trends across several distinct categories, including "PrizeNotification" and "SalesOutreach".}

These LLM-generated tags provide Cloudflare analysts with high-fidelity signals in near real time. Tasks that previously required hours of manual investigation and complex querying can now be surfaced automatically, with relevant context attached. This directly increases the velocity at which we can build new targeted Machine Learning models or retrain existing ones to address emerging behaviors.

Because Cloudflare operates at global Internet scale, we can gather these insights earlier than ever before, often before a new technique becomes widely visible through customer-reported misses.

One of the clearest patterns we’ve identified using this new intelligence is the continued persistence of malicious messages structured to look like Sales Outreach-style phishing. These emails are designed to mimic legitimate B2B communication, often presenting opportunities to purchase or receive "special deals" on unique items or services, to lure targets into clicking malicious links or providing credentials.

Once LLM categorization surfaced Sales Outreach as a dominant vector, we moved from broad visibility to targeted data collection. 

Using LLM-generated tags, we began systematically isolating messages that exhibited Sales Outreach characteristics across our global dataset. This produced a continuously growing, high-precision corpus of real-world examples, including confirmed malicious messages as well as borderline cases that traditional systems struggled to classify. From this corpus, we built a dedicated training pipeline.

First, we curated training data by grouping messages based on shared linguistic and structural traits identified by the LLMs. These traits included persuasive framing, manufactured urgency, transactional language, and subtle forms of social proof.

Next, we focused feature extraction on sentiment and intent rather than static indicators. The model learns how requests are phrased, how credibility is established, and how calls to action are embedded within otherwise normal business conversations.

Finally, we trained a purpose-built sentiment analysis model optimized specifically for Sales Outreach behavior. This avoided overloading a general phishing classifier and allowed us to tune precision and recall for this threat class.

The output of this model is a risk score that reflects how closely a message aligns with known Sales Outreach attack patterns. That score is evaluated alongside existing signals such as sender reputation, link behavior, and historical context to determine whether a message should be blocked, quarantined, or allowed.

This process is continuous. As attackers adapt their language, newly observed messages are fed back into the pipeline and used to refine the model without waiting for large volumes of user-reported misses. LLMs act as the discovery layer by surfacing new linguistic variants, while the specialized model performs fast and scalable enforcement.

This is what an all-out offensive looks like in practice. It is a feedback loop where large-scale language understanding drives focused, high-precision detection. The result is earlier intervention against a threat class that thrives on subtlety, and fewer malicious sales emails reaching the inbox.

The visibility unlocked by LLM-driven mapping fundamentally changed how we improve detections. Instead of waiting for attackers to succeed and relying on downstream user reports, we gained the ability to identify systemic gaps earlier and address them at the source. This shift from reactive remediation to proactive reinforcement translated directly into measurable customer impact.

The most immediate signal of success was a marked reduction in customer friction. Sales Outreach–related phishing has historically generated a high volume of user-reported misses, largely because these messages closely resemble legitimate business communication and often evade traditional rule-based or reputation-driven systems. As our targeted models came online and were continuously refined using LLM-derived insights, fewer of these messages reached end users in the first place.

The data reflects this change clearly. Average daily Sales Outreach submissions — messages that we labeled as clean but were in fact Sales Outreach phishing emails, flagged by end users — dropped from 965 in Q3 2025 to 769 in Q4 2025, representing a 20.4% reduction in reported misses in a single quarter.

This reduction is not just a metric improvement; it represents thousands fewer disruptive moments per day for security teams and end users alike. Each avoided submission is a phishing attempt that was stopped before it could erode trust, consume analyst time, or force a user to make a security judgment mid-workflow. We have seen this trend continue in Q1 of 2026 with average daily submissions decreasing by two-thirds.

In effect, LLMs allowed us to “see” the planes that never made it back. By illuminating previously invisible failure modes, we were able to reinforce defenses precisely where attackers were concentrating their efforts. The result is a system that improves not only detection rates, but also the day-to-day experience of the people relying on it.

Our work with LLMs is just beginning. 

To stay ahead of the next evolution of attacks, we are moving toward a model of total environmental awareness by refining LLM specificity to extract forensic-level detail from every interaction. This granular mapping allows us to identify specific tactical signatures rather than relying on broad labels. 

Simultaneously, we are deploying specialized machine learning models purpose-built to hunt for emerging, high-obfuscation vectors at the "fringes" that traditional defenses miss. By leveraging this real-time LLM data as a strategic compass, we can shift our human expertise away from known noise and toward the critical gaps where the next strike is likely to land.

By illuminating the "planes that didn't make it back," we are doing more than just reacting to missed email; we are systematically narrowing the battlefield. In the email arms race, the advantage belongs to the side that can see the invisible first.

We provide all organizations (whether a Cloudflare customer or not) with free access to our Retro Scan tool, allowing them to use our predictive AI models to scan existing inbox messages in Microsoft 365. 

Retro Scan will detect and highlight any threats found, enabling organizations to remediate them directly in their email accounts. With these insights, organizations can implement further controls, either using Cloudflare Email Security or their preferred solution, to prevent similar threats from reaching their inboxes in the future.

If you are interested in how Cloudflare can help secure your inboxes, sign up for a phishing risk assessment here.