Agentic AI Remediation for Data Security
Slow Remediation Leaves Data Exposed
Mean time to remediate cloud misconfigurations is often measured in weeks, leaving sensitive data at risk far too long.
Manual Fixes Are Slow and Error-Prone
Manual remediation is inconsistent and frequently introduces new configuration issues or breaks dependencies.
Legacy Tools Only Detect - They Don’t Fix
Traditional tools surface issues but rely on humans to investigate and remediate, delaying response when it matters most.
Data Exposure & Access Risk Detection
Matters identifies over-privileged users, exposed cloud storage, and unprotected sensitive data across all environments in real time.
Autonomous Investigation
When a risk is detected, the system doesn’t just generate alerts. It autonomously investigates the context, identifies the relevant data or system owner, and reaches out via email, Slack, or integrated workflows to request approval for remediation.
Priority Assessment
The agent prioritizes fixes based on business impact and actual exposure, ensuring the most dangerous fires are put out first.
Agentic Execution
Once approval is received, the platform automatically executes the fix, such as removing excessive permissions, restricting external sharing, rotating credentials, or enforcing security policies.
Verification & Monitoring
Once a fix is applied, Matters re-scans the environment instantly to ensure the risk is gone and tracks the state to prevent regression.
The Matters Standard
This agentic remediation model combines automation with human approval, reducing manual investigation and reducing MTTR drastically through autonomous policy enforcement.
