300+ Integrations: More Coverage, Less Noise, Better Detections

Monad just crossed 300+ integrations. That includes the sources security teams have relied on for years (CrowdStrike, Okta, AWS, Microsoft, Wiz) and the ones that are quickly becoming critical: AI platforms like Anthropic's Claude and OpenAI. All of them normalized, routable to any destination in your stack, and ready to enrich with 275+ available enrichment sources.

For detection engineers, this is the part that matters: more sources with consistent schemas means more detections you can actually write and ship. Here's what that looks like in practice.

AI platforms are security data sources now

Your engineering teams are making API calls to Claude and OpenAI every day. Those interactions generate audit logs, usage data, and access patterns that your security team needs visibility into. Who's sending what data to which models? Are API keys being used from unexpected locations? Is someone pushing sensitive data through prompt inputs?

CISOs are fielding these questions from their boards right now.

Monad ingests AI platform telemetry into the same pipeline as the rest of your security data. Normalize it, enrich it using any of 275+ available enrichment sources for identity and asset context, and route it to your SIEM or data lake. From there, your detection team can build rules around anomalous model usage, data exfiltration through prompts, unauthorized API key activity, and access patterns that don't match expected behavior. Same workflow as any other source, fully integrated out of the box.

Most pipeline vendors don't have AI platform integrations yet. We built them because customers asked and because this is quickly becoming table stakes for enterprise security programs.

Depth where it counts

You already know that connector count alone doesn't tell the full story. What matters is how far each integration actually goes. That's where we obsess.

For AWS, we don't stop at CloudTrail. We go deep: GuardDuty, Security Hub, VPC Flow Logs, WAF logs, Route 53 resolver logs, S3 access logs, EKS audit logs, Lambda function logs. Every event type, every schema, every quirk — covered. The same applies across CrowdStrike (detection events, incident data, host info, vulnerability findings, identity telemetry), Okta (system events, user lifecycle changes, policy evaluations, threat events), and Microsoft 365 and Entra ID (audit logs, sign-in events, mailbox activity, DLP alerts, compliance signals).

We built this level of depth because our customers told us they needed it. When your team needs full visibility across a platform, surface-level coverage doesn't cut it. That's the standard we hold ourselves to — and it's what keeps our customers' detection pipelines running.

Every connector tested daily

Building an integration is table stakes. Keeping it working is the real job.

Every connector in Monad's catalog is tested daily against live APIs. Schema changes, deprecated fields, new event types, version bumps: we catch them before they break your pipeline. If a vendor ships an API change on a Tuesday, we're not waiting for you to open a support ticket on Thursday. Your data keeps flowing.

This matters more than most teams realize until they've been burned by it. We've talked to security engineers who picked a pipeline vendor partly because they claimed support for a critical source, then discovered during implementation that the "integration" was a webhook template with no field mapping. Or it was on the website but wasn't built yet. That wastes procurement cycles, delays deployments, and forces teams into building custom connectors they shouldn't have to build.

When something is on the Monad integrations page, it's built, tested, and maintained. 300+ integrations that work today.

What this means for your detections

Detection engineers need the right data from the right sources — with consistent field names, enough context to be actionable, and coverage across the kill chain: endpoint, identity, cloud infrastructure, SaaS, developer tooling, network, vulnerability scanners, and AI platforms. They also need to keep the noise out of their SIEM so costs stay under control.

300+ sources feeding into a pipeline that normalizes schemas, filters out the noise, and routes only what matters to your SIEM — with 275+ enrichment sources available to add context along the way. That changes what your detection team can do and what your SIEM bill looks like.

Cross-source correlation gets significantly easier. An Okta authentication event, a CrowdStrike endpoint alert, and an AWS CloudTrail action from the same identity, all normalized to a consistent user field and flowing into your SIEM. userPrincipalName, actor.alternateId, userIdentity.arn — all resolved to user.name before it hits your detection logic.

Coverage gaps become visible. When your pipeline shows you every source flowing through it, you can see what's missing. No VPC Flow Logs? Lateral movement blind spot. No SaaS audit logs? Data exfiltration gap. The pipeline itself becomes an inventory of your detection coverage.

New sources become detections on day one. Add a source to Monad and your detection team can write rules against it immediately. Schema is already normalized, enrichment sources are ready to go. Write the detection and ship it.

What's next

300+ is where we are today. Our roadmap is driven by what customers and prospects tell us they need. If your team relies on a data source you don't see on our integrations page, tell us. That's how most of our integrations get prioritized.

See the full list at monad.com/integrations. If you want to see how these work in practice, with normalized schemas, 275+ enrichment sources, and routing to your existing destinations, start a trial or book a walkthrough with our team.

Your detections deserve the right data from every source that matters. 300+ integrations, filtered and routed so you only pay for what you use.

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