Data

Data handling and encryption

What data the platform holds, where each class lives under each deployment model, which flows reach model providers, and how data is protected, retained, and deleted.

What data exists

The customer data a security review asks about falls into five classes. Naming them precisely matters, because the residency, retention, and access answers below refer to them.

  • Runbooks and institutional context

    Workflow specifications, procedures, accepted examples, and corrections, organized in a filesystem agents can navigate.

    Documented in the FAQ

  • Run traces

    Per-run records: the task, model and tool calls, source references, actions, approvals, and result.

    Documented in the audit-story FAQ

  • Customer content and personal data

    Documents and data your team submits to the platform, including any personal data it contains — categories the DPA's Annex 1 defines, under your control.

    Documented in the DPA, Annex 1

  • Session recordings

    Video and input capture of sandboxed agent sessions, tied to the run's trace. Retention, redaction, and access follow your organization's policy.

    Documented in Agent Sandboxes

  • Operational telemetry

    Quality, cost, and latency measurements joined with evaluation results, used to review routing and workflow performance.

    Documented in Context Inference

One scoping note from the DPA: the Services are not designed for Sensitive Data, and customers agree not to submit it.

Data residency by deployment model

The residency rule is simple because the deployment model does the work: everything in the inventory above lives inside your deployment. Run traces stay in your deployment in every mode. In a VPC, on-premises, or air-gapped architecture, that deployment sits inside your own perimeter — the control plane, execution environments, context filesystem, and run history included, not just inference.

What varies by mode is where the perimeter sits and which flows cross it, and that is documented during implementation. The deployment models page carries the mode-by-mode comparison, including trace residency for each architecture.

Model providers and training

Context does not use one customer's traces, corrections, or institutional context to train models for other customers. That commitment is the platform's own; the second half of the question concerns the model providers a workflow calls.

The platform is model-agnostic — Claude, GPT, Gemini, Kimi, or open weights — and which endpoints are reachable depends on the architecture you select. Each provider's retention and training settings are documented and verified with you during implementation, endpoint by endpoint.

Deployments that cannot tolerate provider egress have structural answers: VPC deployments can use private endpoints so inference traffic does not leave your network, and air-gapped deployments serve open-weight models entirely inside the boundary.

Encryption and the security program

Customer personal data is encrypted in transit using TLS and at rest using industry-standard encryption. Encryption is one item in the written information security program committed to in the DPA's Annex 2. The program also includes:

  • Logical access controls: role-based access, least-privilege provisioning, multi-factor authentication for administrative access, and prompt revocation when roles change.
  • Network and infrastructure controls: segmentation, firewalling, and hardened configurations with leading cloud providers.
  • Vulnerability management: periodic scanning, patching, and third-party penetration testing.
  • Logging and monitoring designed to detect unauthorized access.
  • Personnel measures: background checks where permitted by law, confidentiality obligations, and security awareness training.
  • Business continuity and disaster recovery, with backups and periodic testing.
  • A documented incident response plan covering identification, containment, investigation, remediation, and notification.

The full annex is public in the Data Processing Addendum, and the combined security policies behind it are available in the document library.

Retention and deletion

Operational retention — how long traces and recordings stay queryable, what gets exported to your systems — is configured for your deployment, because audit and records requirements differ by customer and regulator.

Contractual deletion is defined in the DPA: when the agreement ends, Context deletes or returns customer personal data at your election made within thirty days, except where law requires retention or the data sits in routine backups — backup copies remain protected under the DPA and are deleted on the standard deletion schedule.

The DPA also covers the operational edges of data handling: assistance with data subject requests, and notification without undue delay if a personal data breach affects customer personal data, with the information needed to meet your own notification obligations.

Request documentation

The document library covers audit reports, assessments, and procurement paperwork. Request what your review needs; anything that requires an NDA is shared once it is in place.

security@context.ai

Also documented: Deployment models · Identity & authorization · Compliance program

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