Cyera shows you the risk. Teleskope reduces it.

Highly accurate classification (low false positive or negatives) that scales across structured and unstructured data of various formats, which enables high-confidence automation of risk reduction.

• Multi-stage AI pipeline: lightweight ML models route to specialized SLMs and LLMs, which combine contextual reasoning, semantic understanding, and business-specific policy signals
• Classifies a broad array of entity types (PII/PHI/PCI, credentials/secrets, custom data types)
• Classifies document types (e.g., IP, payroll, etc.) and provides redacted AI-generated summaries
• Differentiates dormant sensitive data from actively used or broadly accessed data, enabling prioritization
• Associates classified data with business relevance (e.g., customer vs employee data) to drive enforcement.

Fast, relatively low-noise visibility into sensitive data across cloud environments, but classification is primarily optimized for discovery rather than enforcement. As a result, findings require additional interpretation before action can be confidently automated.

• Cloud-native, agentless architecture optimized for SaaS API scanning
• Sampling-based scanning approach to accelerate time to insight, which however may lead to false negatives
• Machine learning combined with metadata analysis for classification
• Lower false positives compared to legacy, regex-heavy DSPM tools, however true positives may be irrelevant to the business
• Limited built-in prioritization based on business criticality
• Classification outputs primarily feed dashboards and external workflows

Reduces risk automatically and continuously, without tickets, external tools, or manual follow-through, resulting in materially faster time to risk reduction and lower operational burden on security teams.

• Robust library of native actions: access revocation/scoping, sharing restriction, redaction/masking, encryption, cleanup of overexposed/stale sensitive data
• Embedded into existing workflows
• Works for existing exposure and in real time
• Supports full automation or human-in-the-loop based on risk and confidence

Remediation is largely dependent on integrations and customer-built workflows, which can delay consistent risk reduction after discovery.

• Limited native remediation actions
• Most enforcement executed through external orchestration tools (e.g., SOAR, Tines)
• Integrations require custom logic, scripting, and ongoing maintenance
• Revocation and access changes limited to supported cloud permissions
• No deeply embedded, policy-driven automation

Organizations can safely adopt AI tools and agentic workflows because sensitive data is automatically controlled and cleaned up as it is accessed and used.

• Detects and controls access to data at rest and in use by AI tools, copilots, and agents
• Prevents overexposed or non-compliant data from being used in AI workflows
• Enables AI adoption without requiring manual approvals or separate governance tools
• Supports continuous enforcement as AI usage evolves

Customers receive visibility into AI-related data exposure and AI readiness posture, but enforcement of AI data usage policies relies on external workflows rather than native, real-time control.

• Identifies sensitive data that may be accessed by AI systems
• Provides AI posture dashboards and reporting
• No native, in-platform control of AI prompt or response behavior
• Remediation of AI-related exposure handled via integrations

Deploys in hours to days across cloud, SaaS, and on-prem without agents, enabling faster time to value and consistent risk reduction across hybrid environments.

• Agentless, API-driven architecture across SaaS, cloud, and AI data flows
• Flexible deployment (SaaS or self-hosted) with on-prem scanning
• Single control plane across hybrid environments

Customers benefit from rapid onboarding and quick time to insight in cloud-first environments, but hybrid or on-prem expansion can introduce additional architectural complexity.

• Agentless, SaaS-first architecture leveraging cloud APIs
• Rapid deployment in supported cloud and SaaS environments
• Minimal infrastructure footprint for initial rollout
• On-prem environments require additional components (e.g., outposts)
• Expansion into complex enterprises may require phased deployment

Support model is tied to business outcomes: every engagement starts with the outcome alignment.

• Every customer is paired with a hands-on customer engineer who actively helps define use cases, configure policies, and drive outcomes.
• Customer work is structured around business objectives (e.g., insider threat reduction, IP protection, audit streamlining) and measured through POC discovery, kickoff plans, and quarterly outcome reviews.

Customers receive onboarding support, but long-term operational success often depends on the customer's ability to build and maintain remediation workflows independently.

• High-touch engagement during proof of value
• Focus on accelerating time to initial discovery
• Less structured outcome-driven engagement post-deployment
• Assumes customers operationalize insights via integrations
• Remediation workflows typically customer-built or partner-led
• Support model more reactive than continuous partnership