Behavior, not signatures.
Per identity. Filtered by the graph.
UEBA has a reputation. Some of it earned — most cloud UEBA tools poison themselves with noise because every alert fires standalone. Cloudanix is different by construction. Anomaly signal is just one input; the same graph used for posture, identity and threat-intel decides whether an anomaly is the start of a story or normal-for-this-identity drift.
Why most UEBA disappoints — and how the design changes.
Three failure modes you've probably felt.
Noise floor too high
Most UEBA fires every statistical outlier. In a real cloud, hundreds of "anomalies" per day are normal-for-someone-somewhere. Without the right filter, the SOC stops looking.
Baselines collapse on identity churn
Cloud identities — especially service accounts and AI agents — change shape constantly. New roles attached, scopes adjusted, deployments re-keyed. UEBA tuned to static identity profiles fails the moment the identity legitimately evolves.
No context for why an anomaly matters
"User X did action Y, which they haven't done in 90 days" is a sentence. "User X did action Y from a flagged ASN, against a resource the user can pivot from to PII storage" is a finding. Most UEBA stops at the sentence.
Per-identity baselines, graph-filtered alerts.
Per-identity baseline
Every identity — human, service account, CI/CD principal, AI agent — gets its own rolling behavior baseline. Actions performed, resource types touched, source ASN distribution, time-of-day windows, peer-group similarity. Baselines refresh continuously and adapt to legitimate identity evolution.
Anomaly detection (examples include…)
We ship many detector classes — examples include unusual-action anomalies (a role used to read S3 suddenly writing IAM policies), source anomalies (auth from a new geography or flagged ASN), volume anomalies (sudden multi-thousand-API-call bursts), peer anomalies (an identity doing things its peer group never does), credential anomalies (long-dormant key suddenly active). The detector set evolves continuously — we don't enumerate them as finite.
Graph filter
An anomaly's raw score is only the start. The graph evaluates blast radius (what could this identity reach if compromised?), threat-intel context (is the source ASN or IP on a feed?), data sensitivity (does this anomaly involve a PII/PHI resource?), and posture context (is this identity already over-privileged?). The alert that surfaces has all of that attached.
Quiet by design
Suppression isn't bolt-on; it's first-class. Statistical anomalies that are graph-normal don't fire. The signal-to-noise ratio is the metric we optimise — not the absolute count of anomalies detected.
Three architectural choices that change the outcome.
Built on the graph, not next to it
UEBA isn't a sidecar product — it reads from and writes to the same asset graph as posture, identity and threat-intel. Every alert has graph context attached by default.
Identity-class-aware
Different baselines for different identity classes — humans behave differently from service accounts differently from AI agents. Treating them all the same is why generic UEBA struggles in cloud.
Detector set evolves continuously
The detector classes aren't a fixed list — we add new ones as cloud-native attack patterns emerge. Customers don't have to re-tune.
What Our Users Are Saying
Customer Reviews
Cloudanix is trusted by security leaders worldwide to deliver proactive, reliable, and cutting-edge cloud security.
One day, I changed the password of a root account, and my CTO called me within less than a minute to confirm if I did so. I was not expecting a reaction this quick. He told me Cloudanix alerted him of this password change and that he wanted to confirm as it was a critical security notification. I couldn't believe it!
Compliance is one way of staying secure, but what I want is the ability to go deeper and attain 'true security.' Cloudanix provides us the capability to do so.
Cloudanix is building for the future of the cloud, which makes the product all the more desirable.
Cloudanix gave us the visibility we were missing. Being able to move from permanent access to a robust Just-In-Time (JIT) workflow has fundamentally changed our security posture without slowing down our engineering velocity.
We are excited to leverage Cloudanix's comprehensive multi-cloud DevSecOps solution to secure our production workloads on AWS. Cloudanix has demonstrated that it can solve many challenges that DevSecOps teams face while continually adding new features such as SOC2 compliance and drift detection.
Managing third-party partner access was once a major concern for our security posture. With Cloudanix JIT Cloud, we've effectively achieved zero third-party risk. We can now grant access confidently, knowing that it is temporary, audited, and automatically revoked, resulting in a 100% reduction in our privileged access exposure.
The snooze feature and responsible alerts have helped us save time and prioritize what to tackle first.
Implementing Cloudanix JIT internally allowed us to practice what we preach. By eliminating permanent access to our own clouds and databases, we've neutralized the risk of standing privileges, ensuring our own 'keys to the kingdom' are never left exposed.
The problem with permissions is a lot of times, the gaps are left open due to oversights from inside the organization itself. With Cloudanix's CIEM, we get a complete view of user permissions and access. This enables us to update the permissions, reducing the attack surface.
In the world of Fintech, trust is our currency. Cloudanix provided the frictionless visibility we needed to secure our EKS workloads across AWS, ensuring we stay audit-ready for SOC2 and GDPR without slowing down our engineering velocity.
Cloudanix delivered value within 5 minutes of onboarding. Continuous monitoring, timely detection, and excellent documentation helped us attain a great cloud security posture.
Technology strategies and business strategies are in a state of constant change which includes centralization and decentralization of responsibilities. Regardless of strategic shift, we still have intellectual property to protect. Cloudanix are critical partners for us in our public cloud security posture across our three cloud providers.
Cloudanix has been amazing. They opened up a common Slack channel with us — and it feels like we are talking to our own team and getting things done with Cloud security. The support team is always available, friendly, helpful, and ready to go out of their way.
Beyond just access management, Cloudanix CSPM has given us a unified view of our AWS environment. The real-time alerting and anomaly detection allow us to prevent any untoward activity before it happens, which is critical for a marketplace connecting 50+ financial institutions.
For a Fintech company, data is our most valuable — and most sensitive — asset. Cloudanix DAM hasn't just improved our visibility; it has given us control. The ability to mask data and prevent unauthorized queries in real-time is a game-changer for our compliance and customer trust.
Our clients, especially in the Middle East financial sector, demand absolute accountability. Cloudanix JIT Cloud has been a competitive differentiator for us, allowing us to provide secure, governed access to customer accounts that meet their strictest audit and compliance requirements.
Cloudanix is always on my team's lips because of its exceptional support. Be it a small or big query, Cloudanix has gone above and beyond to resolve them. This one's a keeper for us.
For a long-lasting partnership, great support goes a long way. Cloudanix has delivered exceptional support whenever required. Their edge is their team is always ready to go beyond to solve any issues that we have. This speaks volumes about the culture at Cloudanix.
Beyond the technology, Cloudanix feels like an extension of our own team. Their willingness to stand up a dedicated Middle East tenant for us and provide exceptional support at a sensible price makes them a long-term partner for Hugosave.
The real-time notifications that Cloudanix provides are a real lifesaver. Their adaptive notifications ensure that my team stays productive and doesn't get interrupted all the time.
The whole point in technological evolution is to help improve the world we live in. We must protect that and to do so requires an effective and efficient security strategy. The Cloudanix team helped make our public cloud security posture management strategy a reality. The symbiotic relationship we have allows for a continuous feedback loop which is how business should operate.
Cloud UEBA, asked plainly.
Is Cloud UEBA a separate product or part of the CNAPP?
Part of the CNAPP+. We ship UEBA as part of the Cloud Detection & Response capability — it's not a separate purchase or a sidecar. It shares the same asset graph, identity graph, and threat-intel pipeline as everything else in the platform.
How does UEBA handle service accounts and AI coding agents — they change behaviour constantly?
Identity-class-aware baselines. Humans get one kind of baseline (peer-group similarity, working-hours, action-class distribution). Service accounts get another (usually narrower in scope, with deployment events factored into baseline-reset signals). AI coding agents get a third — we baseline against the agent identity AND the operating human (cross-baseline anomaly when the agent does something the human never asks for, or vice versa). When a legitimate identity evolution happens (new role attachment, scope change), the baseline absorbs it rather than firing on every action thereafter.
What kinds of anomalies does it detect?
We ship many detector classes; this list is a sample, not exhaustive — and we add to it as cloud-native attack patterns emerge. Examples include: unusual-action anomalies (an identity doing actions it never did before), source anomalies (authentication from a new geography, ASN or device fingerprint), volume anomalies (sudden multi-thousand-API-call bursts), peer-group anomalies (an identity doing things its peer cohort never does), credential anomalies (long-dormant credentials suddenly active), session anomalies (impossible-travel patterns, parallel sessions from disparate locations). The detector set evolves continuously based on what the field surfaces.
How do you avoid the UEBA "noise floor" problem?
Two design choices. (1) Suppression is first-class, not bolt-on. Anomalies that are statistically outliers but graph-normal (no blast radius, no sensitive data reach, no threat-intel correlation) don't fire to the analyst queue — they're suppressed with full audit trail. (2) Scoring is multi-input: anomaly probability is one input, but graph context (blast radius), threat intel (ASN reputation), data classification (PII/PHI exposure), and posture context (identity over-privileged?) all weigh in. An alert that surfaces has the why baked in.
How does UEBA interact with threat intelligence?
They reinforce each other. A behavioral anomaly committed from a flagged ASN scores higher than the same anomaly from a clean source. A UEBA-anomalous user accessing a resource that was recently matched against an IOC promotes both signals. The two are correlated, not separate alert streams.
Does Cloudanix UEBA replace my SIEM's UEBA module?
For cloud identities and cloud actions, yes — typically more accurate because we have the asset graph the SIEM doesn't. For on-prem and endpoint UEBA (workstation behavior, AD logons, file-server access patterns), we don't replace your SIEM's UEBA — those signals live in tools optimised for that space. Many customers use both: SIEM UEBA for endpoint and on-prem, Cloudanix UEBA for cloud identities, with cross-correlation where helpful.
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