When CSPM and SIEM are separate, analysts maintain two investigation contexts: cloud posture findings in one tool and security alerts in another. Correlation between them is manual. A finding that appears in one tool rarely gets enriched with data from the other before a decision is made. Panther unifies both in a single platform, where every cloud posture finding flows into the same data lake, investigation workflow, and AI triage pipeline as the rest of your security telemetry. The result is that cloud posture stops being a compliance checkbox managed in a separate system and becomes an integrated part of how your team actually detects and responds to risk.

Most compliance frameworks require evidence that cloud environments are configured securely and monitored continuously. Panther's policy-as-code framework can be mapped to specific CIS benchmark controls, SOC 2 requirements, and other framework requirements, so each policy check generates both a security finding and a compliance data point. Because policies run continuously and every result is logged with a timestamp, the compliance evidence is generated automatically rather than assembled manually before an audit. Security teams that previously spent weeks reconstructing cloud configuration history for auditors can pull that evidence on demand.

CSPM tools routinely surface hundreds of findings across a large cloud environment, most of which are low-severity configuration drift rather than active risk. Without prioritization, teams either ignore the findings queue or spend time on low-value items while higher-priority issues wait. Panther's AI agent triages cloud posture findings the same way it handles alerts: gathering context, correlating with log data, assigning a risk classification, and surfacing the ones that warrant immediate attention. Findings that don't require action get documented but don't consume analyst time. Teams using Panther with Wiz or Orca find that connecting the two tools doesn't create more work — it creates a manageable, prioritized workflow from a previously unstructured findings backlog.

Policy-as-code means writing cloud security policies as Python — stored in GitHub, reviewed through pull requests, tested with CI/CD, and deployed the same way your engineering team ships code. When policies live in a GUI, they're hard to audit, difficult to version, and invisible to the rest of your security tooling. Python policies are transparent: any engineer can read exactly what a policy checks, understand why it fired, and propose a modification. They're also readable by AI, which means Panther's agent can trace a finding back to the specific policy and propose an update if the check needs refinement.

Your CSPM tool excels at scanning cloud resource configurations. Panther adds two things it can't do alone: it brings those findings into the same investigation workflow as your other security alerts, so analysts are working one queue rather than two separate tools, and it enables correlation between posture findings and log data. A misconfigured S3 bucket finding becomes significantly more actionable when the AI agent can correlate it with CloudTrail logs showing whether that bucket has been accessed recently, by whom, and whether any of those access patterns look suspicious.

Panther scans your AWS environment daily using policy-as-code to check resource configurations across S3, IAM, EC2 security groups, VPCs, and other services. Each policy is a Python rule that defines what compliant configuration looks like and fires when a resource deviates from it. Because policies are written in the same framework as detections, they benefit from the same version control, CI/CD, and peer review workflow your detection engineers already use. Real-time monitoring supplements the daily scan, flagging misconfigurations the moment they appear rather than waiting for the next scheduled run.

Cloud security posture management (CSPM) involves continuously scanning your cloud environment for misconfigurations — overly permissive S3 buckets, IAM roles with excessive privileges, exposed security groups — and surfacing them as findings. A standalone CSPM tool does the scanning well, but findings sitting in a separate dashboard don't close themselves. Without a connection to your security operations workflow, analysts don't see the findings, priorities don't get triaged, and remediation depends on someone manually checking a tool that most security teams don't have time to monitor consistently.