Security posture for an autonomous AI data scientist.
OctOpus automates recurring data-science work for enterprise teams: give it data and a business objective and it builds, validates, and delivers the model and report — without requiring a data scientist to operate it. Because it runs that loop on your data, it is built for regulated enterprise workloads. The agent's research loop is sandboxed, secrets are scrubbed before the LLM-authored training code runs, holdout data lives outside the agent's workspace, and every research run is fully audit-logged. For SOC 2-, GDPR-, HIPAA-, and PCI DSS-aligned teams, Desktop and VPC deployments keep data on your perimeter.
Data residency
Desktop (local)
OctOpus Desktop runs the agent fully on a workstation or on-prem server. Data, models, training scripts, and logs stay on the device. The only outbound connection is to the LLM endpoint you configure — Anthropic, OpenAI, Azure OpenAI, Bedrock, or a private model.
VPC / private cloud
OctOpus Enterprise deploys inside your AWS, GCP, or Azure account as a private install. Customers integrate with IAM / Workload Identity / Entra ID, route inference through Bedrock or Azure OpenAI, and keep all data inside their network perimeter.
SaaS (cloud)
The hosted product runs in a single, audited region with at-rest disk encryption and TLS-in-transit. Tenant isolation is enforced on every endpoint; every run is gated by an ownership check. Holdout is stored outside the agent workspace.
Encryption
- In transit. TLS 1.2+ for all client and inter-service traffic. HSTS enforced on production endpoints.
- At rest. Persistent volumes use the storage provider's at-rest encryption (AES-256 on AWS / GCP / Azure managed disks). The Desktop app uses OS-level disk encryption (FileVault / BitLocker / LUKS recommended).
- User-provided LLM and connector keys. Encrypted at rest with Fernet using a per-install random secret. Decrypted only inside the server process, never written to disk plain, never shipped to log streams.
Identity, access, and tenancy
- SSO. SAML 2.0 and OIDC with Okta, Microsoft Entra ID (Azure AD), Google Workspace, OneLogin, JumpCloud, and any standards-compliant IdP.
- SCIM. SCIM 2.0 provisioning for user and group lifecycle.
- RBAC. Role-based access control with workspace isolation. Every API endpoint enforces an ownership check before serving runs, models, or logs.
- Audited multi-tenancy. Tenant isolation is enforced server-side; client roles are never trusted alone.
Agent sandboxing and secret handling
- Subprocess scrub. Every research-run subprocess is launched with a scrubbed environment — provider API keys are removed before the LLM-authored
train.pycan reados.environ. - Holdout isolation. The holdout dataset is stored outside the agent's workspace. The LLM cannot glob or read it during planning or experiment generation — the orchestrator scores the winner on it only after the run is locked.
- Log redaction. All server-sent-event and SQLite log streams pass through a secret-redaction regex before reaching disk or the UI. Provider keys, connector credentials, and known secret patterns are masked.
- Production refusal. The server refuses to boot in production mode without persistent storage and a configured Postgres instance — guards against ephemeral, unencrypted, or single-node deployments by mistake.
Audit and Model Risk Management (MRM)
- Full audit log per workspace: research plan, every experiment's train.py, every error, every revision, holdout metrics, deployed artifact hash.
- Exportable for SR 11-7 / IFRS 9 / CECL / SOC 2 reviews.
- Every train.py is human-readable — no opaque AutoML pipeline blocks to defend in committee.
- Reproducible runs: rerun the same dataset with the same seed and the same OctOpus version and get the same model.
Compliance alignment
| Framework | How OctOpus aligns |
|---|---|
| SOC 2 Type II | Encryption at rest and in transit, access logging, role-based access control, audit logging, secret redaction, change management. Documentation available under NDA. |
| GDPR | Data residency controls via Desktop / VPC, audit trail of processing, scoped retention, support for right-to-erasure on a per-workspace basis. |
| HIPAA | Desktop or VPC deployment for PHI; the agent never transmits PHI outside your perimeter when configured with a private inference endpoint. |
| PCI DSS | VPC deployment so cardholder data never leaves your perimeter; encrypted-at-rest secrets; redacted log streams. |
| SR 11-7 / IFRS 9 / CECL | Full audit log of model development; inspectable train.py per experiment; reproducible builds for MRM independent validation. |
| AML transaction monitoring | VPC deployment for transaction data; calibrated probability outputs; SHAP attributions for case-investigator workflows. |
Responsible disclosure
If you discover a security issue, email security@octoopus.dev. We acknowledge within 24 hours and patch coordinated-disclosure issues within 7 days for critical and 30 days for high-severity findings. We do not pursue legal action against good-faith researchers who follow responsible disclosure.