Security, Privacy, and Zero Trust Architecture
Assume every component can be hostile.
“Security is not a feature. It is an emergent property of an architecture where every boundary is explicit.”
Security as a platform property
AlgoLens executes user-controlled code, stores educational and organizational artifacts, exposes APIs, streams execution events, and may integrate with AI providers. That threat model is serious from day one. The platform must assume user code is malicious, clients are untrusted, plugins may be compromised, tokens can leak, dependencies can be attacked, and tenants must never access each other's traces.
Zero trust means every layer validates inputs, limits capabilities, scopes credentials, audits sensitive actions, and denies by default. Sandboxes prevent filesystem, network, process, secret, and environment access unless explicitly permitted. API permissions isolate organizations, projects, traces, exports, and billing resources. Service accounts, key rotation, least privilege, rate limiting, anomaly detection, and audit logs are baseline requirements.
Privacy also matters. Traces can contain source code, inputs, comments, and learning history. The platform should support retention policies, deletion workflows, export controls, private sharing, enterprise data boundaries, encryption at rest and in transit, and clear AI data usage settings. Security is not a mode; it is the architecture.
User Code
↓
Validator
↓
Static Safety Analysis
↓
Sandboxed Runtime
├─ Allowed APIs
└─ Forbidden APIs
↓
Audited Trace Events