LLMs inside the platform.
Humans on the consequential edges.
Cloudanix uses LLMs throughout the product to do the boring, slow, error-prone work nobody enjoys — mapping regulations to controls, translating English into queries, drafting detector logic, summarising alerts, proposing remediations. Anything that commits to your environment goes through human review. LLMs do the typing. Humans do the deciding.
Augmentation, not autonomy.
We don't ship an "AI SOC analyst" that closes your tickets. We ship LLM-native assistance throughout the product so your team types less and decides more. Anything LLMs output is reviewable. Anything that commits to your environment requires explicit human approval.
Six places LLMs already make Cloudanix faster.
All with human review for anything consequential. None of these use your data to train shared models.
Compliance mapping
Map a regulation section (RBI, SAMA, DPDPA, DORA, etc.) to applicable Cloudanix controls in seconds, with a human approval step before the mapping commits to your compliance scope.
Plain-English query (Text2SQL)
"Show me every RDS instance created in the last 7 days without encryption" — translated to a graph query, executed, results returned. SQL stays available for power users.
Detector / rule authoring assist
Describe a posture rule or detector class in English; the LLM drafts a candidate. Your security team reviews and refines before the rule goes live in your environment.
Alert summarization
Long alert payloads (graph context, timeline, affected resources, identity history) compressed into a paragraph an analyst can read in 15 seconds. The full payload remains one click away.
Remediation suggestions
For a given finding, the LLM proposes one or more remediation options — Terraform diff, IaC patch, CLI command — that an engineer reviews and applies (or rejects) through the standard workflow.
Documentation generation
Auto-drafted runbooks, compliance evidence narratives, on-call escalation summaries — generated from the same graph the alert came from. Reviewed and stored by your team, not by us.
How LLMs interact with your data.
Your data is your data
Customer telemetry, findings, queries, and reviewed outputs are not used to train shared / multi-tenant LLM models. Per-tenant model behavior tuning, where it exists, stays within your tenant.
Configurable model surface
Enterprise deployments can choose which LLM providers are in scope (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock with your-region constraint). Air-gapped / sovereign deployments support self-hosted models on request.
Full audit trail
Every LLM-assisted action — prompt, model used, output proposed, human decision (approve / edit / reject), final committed state — is logged with the same audit cadence as everything else in Cloudanix.
No autonomous writes
LLMs never directly modify your cloud environment or your Cloudanix policy state. They propose; humans approve; Cloudanix executes through the same workflow as manual changes — same audit, same RBAC, same rollback.
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.
LLMs in Cloudanix, asked plainly.
Are LLMs taking actions in our cloud account?
No. LLMs in Cloudanix propose; humans approve; Cloudanix executes through the same workflow as a manual change. No autonomous writes to your cloud environment, no autonomous modifications to your Cloudanix policy, no autonomous escalation of access. Every LLM-assisted action has a reviewable proposal step before anything commits.
Is our security data used to train AI models?
No. Customer telemetry, findings, queries and reviewed outputs are not used to train shared / multi-tenant LLM models. Per-tenant tuning, where it exists, stays inside your tenant boundary. Specific data-handling commitments are part of the standard DPA on enterprise contracts.
Which LLM providers does Cloudanix use?
Default tenants use a configurable mix that typically includes OpenAI, Anthropic, and the relevant hyperscaler's hosted endpoint (Azure OpenAI for Azure-resident customers, Bedrock for AWS-resident). Enterprise contracts can pin the model surface to specific providers / regions / endpoints based on procurement requirements. Air-gapped / sovereign deployments can be configured with self-hosted open-weight models on request.
What if the LLM proposes something wrong?
Then your human reviewer rejects or edits it before it commits. That's the design. We don't trust LLM output for consequential changes — the review step is the contract. False positives in LLM output cost a reviewer 30 seconds; false negatives in human-only workflows cost weeks of compliance debt. Combining the two is faster than either alone.
How is this different from an "AI SOC" that runs autonomously?
Different intentionally. The "AI SOC analyst" pattern moves faster on the easy 80% of incidents but tends to be opaque on the consequential 20%. Cloudanix's stance is that the consequential edge is where humans matter most — the LLM saves them the typing, the human keeps the judgement. Customers who want full-autonomous SOC patterns are better served by tools explicitly designed for that posture.
How does this interact with our own AI governance?
Cleanly. Every LLM call originated by Cloudanix is logged in your audit pipeline (the same one that logs every other Cloudanix action), so AI governance auditing can verify which models were used, on what data, at which times. For organisations with strict AI-use policies (regulated industries, public sector), the model surface and data scope are both contractually configurable.
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