BigID catalogs your 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.

Customers obtain very broad classification inventories suited for compliance and governance initiatives, but struggle to operationalize results for continuous risk reduction due to workflow complexity and limited prioritization logic.

• Strong coverage across many data types and sources
• Classification primarily driven by regex and pattern matching
• Limited ML beyond out-of-the-box models (English and Spanish)
• Moderate noise levels that increase at scale
• Outputs optimized for reporting and labeling rather than enforcement
• Less precision around "what matters now" versus "what exists"

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.

• 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 governance-driven and relatively slow, making it suitable for compliance workflows but less effective for continuous, operational risk reduction.

• Remediation executed through governance applications (retention, deletion, labeling)
• Strong approval and review mechanisms before action
• Limited real-time or automated enforcement
• If-then posture violation model rather than dynamic prioritization
• Actions are often irreversible (e.g., deletion)
• Best suited for episodic compliance events rather than continuous operations

Organizations can safely adopt AI tools and agentic workflows without increasing data exposure, because sensitive data is cleaned up in real time as it is used by AI systems.

• 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 can document and govern AI-related data usage for compliance purposes, but cannot enforce controls dynamically as AI systems operate.

• Classifies and labels data intended for AI use
• Supports policy review and approval for AI datasets
• Limited real-time enforcement once AI workflows are live
• Human-driven governance over automated control
• Better suited for audit readiness than continuous AI risk reduction

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 can deploy across a wide range of data sources, but often experience longer rollout cycles, higher dependency on services, and inconsistent time to value due to platform complexity.

• SaaS deployment only, no on-prem
• Connector-heavy, multi-module architecture
• Broad structured and unstructured data source coverage
• Multiple services components increase upgrade complexity
• Higher dependence on professional services for rollout and tuning
• QA and configuration overhead increases with scale

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.

Onboarding has a limited defined scope. After that, it requires additional billable services or third-party partners, and ongoing support is largely break/fix.

• Support model is tightly coupled with professional services
• Significant time spent on tuning, suppression, and alert management
• Ongoing changes (new data stores, new users, new permissions) frequently require renewed engagement
• Supports troubleshooting, but ownership of outcomes remains with the customer