As organizations face increasing regulatory, privacy, and governance pressure, selecting a data security platform that delivers both visibility and operational impact is critical.
Built-in classifiers are regex based and produce millions of false positives
Labeling requires extensive tuning
Automation across third-party systems is limited
Custom classifiers are difficult to build and maintain
Retention and disposition workflows are difficult to automate at scale
AI-driven, context-aware scanning across millions of files in hours, not weeks
Understands what data represents (contracts, IP, HR records), not just patterns
Business-level categorization via advanced ML
Higher precision enables confident automation
Prioritizes what matters instead of surfacing raw volume
Classification becomes actionable and trusted.
Long and tedious production integration process and no autodiscovery, even for Azure
Custom SITs, EDM, and trainable classifiers require extensive setup and ongoing tuning
Built-in classifiers are regex based and produce inconsistent precision at scale
Limited to files under 20MB
Millions of labeled files still require manual validation
Low confidence in accuracy limits automated enforcement.
Classification becomes a maintenance burden rather than a scalable control.
Automatically applies and updates MIP labels with high-confidence detection
Labels travel with documents across Microsoft and third-party systems
Activates DLP, SASE, and CASB controls immediately
Detects and alerts on unauthorized classification changes
Turns labeling into a dynamic enforcement signal
Labels become operational and reliable.
MIP labels are based on regex classifier and are generic and inaccurate
Inconsistent labeling across large data estates
Manual oversight required to maintain confidence
Silent declassification risks remain
Enforcement policies limited by trust in metadata
Labels exist, but enforcement is constrained.
Automatically enforces policy in real time
Revokes access, restricts sharing, and cleans up exposure continuously
Supports scalable retention automation
Reduces material exposure without proportional headcount growth
Shifts from reactive alerts to measurable risk reduction
Risk reduction becomes continuous, not episodic.
Purview alerts flow into SIEM and SOAR workflows; only specific actions are supported (vs an entire workflow)
Remediation depends on tickets, scripts, and analyst capacity
No interactive / UX based remediation interfaces; users can't find what's flagged
Reducing exposure across millions of files takes months
Retention and disposition processes are approval-heavy
Risk visibility improves, but reduction lags.
"Teleskope was the only solution that did everything we needed, from data deletions to vendor coordination and PII detection. The platform has become a staple across our data security workflows."
No. It runs alongside Purview. Purview classifies your Microsoft estate, Teleskope enforces and remediates on top, and extends coverage beyond Microsoft.
Action. Purview tells you where sensitive data lives; Teleskope fixes the exposure and over-permissioned access automatically, with no tickets and no added headcount.
Yes. It respects your Purview labels and classifications, so nothing gets rebuilt.
Teleskope extends the same discovery and enforcement across AWS, GCP, on-prem, SaaS, and unstructured data. One policy, all of it.
It remediates automatically and continuously, so findings don't pile up in a backlog.
Not hard. It connects without disrupting Purview or re-labeling data, and starts reducing risk in days.