The trust paradox
Every meaningful audit—human or machine—requires visibility. Finance, operations, and product systems hold the signals that expose waste, risk, and opportunity. Yet the same depth that makes an AI audit powerful can make security and legal teams uneasy. The central tension is straightforward: you must share sensitive data to prove you are handling sensitive data well.
Resolving that paradox demands more than marketing assurances. It requires architectures where confidentiality, integrity, and availability are engineered defaults; where retention is minimal by design; and where customers can verify—not merely believe—how their information moves through the stack.
“The question is not whether you will share data with an auditor—it is whether the audit platform earns the same bar of evidence you would demand from any critical vendor touching your crown jewels.”
How Stratoscan protects data
Stratoscan’s security posture begins with end-to-end encryption. Data is encrypted at rest using AES-256 and protected in transit with TLS 1.3, enforced across administrative consoles, ingestion APIs, and internal service meshes. Keys are managed through hardware-backed or cloud-provider KMS patterns with strict separation of duties so that no single role can both approve access and decrypt arbitrary customer payloads.
Complementing transport and storage protections is a zero-retention policy for raw customer content used in audit analytics: once processing and report generation complete within contractually defined windows, primary datasets are purged or returned according to the customer’s configuration. Derived artifacts that must persist for dispute resolution—such as signed audit summaries—are stored with the same encryption standards and scoped access as source inputs.
Architecture overview: region, isolation, and training boundaries
Geography matters as much as cryptography. Stratoscan’s default architecture ensures that customer data does not leave the client’s chosen region during processing unless explicitly authorized for cross-border support cases covered by supplementary agreements. Analysis runs inside isolated containers with ephemeral disks, reducing the risk of co-tenant leakage and limiting the blast radius of any single job.
Critically, Stratoscan does not train general-purpose machine learning models on client data. Models that power benchmarking and anomaly detection are developed on synthetic, licensed, or aggregated datasets; customer-specific tuning, when offered, uses privacy-preserving techniques and contractual carve-outs so intellectual property and personal data are not absorbed into shared weights without consent.
Compliance framework
Independent assurance anchors customer trust. Stratoscan maintains a control environment aligned to SOC 2 Type II, ISO 27001, GDPR, and, for healthcare-adjacent engagements, HIPAA-compatible safeguards including business associate agreements where applicable. These frameworks are not checklists to “complete once”—they structure how access reviews, change management, incident response, and vendor risk assessments run quarter after quarter.
Access controls and observability
Human access follows role-based access control (RBAC) paired with multi-factor authentication (MFA) for every interactive session. Just-in-time elevation replaces standing privilege for rare troubleshooting events. Audit logs capture authentication, data queries, configuration changes, and exports with tamper-evident storage so security teams can reconstruct who touched what, when, and why.
Across engineering and support, Stratoscan applies the principle of least privilege: default-deny network policies, scoped service accounts, and automated entitlement reviews that revoke access when roles change. The goal is not merely compliance but operational discipline—reducing the probability that a single compromised credential becomes a systemic breach.
Third-party security
Modern SaaS is a graph of subprocessors. Stratoscan performs vendor assessments on any provider that could process customer data or authenticate users, evaluating SOC reports, encryption posture, incident history, and data processing agreements. Sub-processor transparency is published and updated so legal teams can map flows without chasing ad-hoc spreadsheets during diligence.
What clients can do
Security is bilateral. Customers strengthen outcomes when they:
- Prepare minimized datasets—masking fields that are not material to the audit scope while preserving analytical fidelity.
- Align access management with the principle of least privilege for integration accounts used during ingestion.
- Enable monitoring and alerting on unusual API usage or bulk exports from connected systems.
These practices do not shift liability; they reduce noise, accelerate audits, and keep blast radii small if an upstream credential is exposed.
Our commitment: responsible AI and continuous testing
Beyond infrastructure, Stratoscan invests in responsible AI practices: human review pathways for high-impact findings, bias and robustness testing for scoring components, and clear documentation so customers understand model limitations. Regular penetration testing by independent firms exercises external and internal attack surfaces, while a bug bounty program invites coordinated disclosure from the research community.
AI auditing should illuminate risk—not introduce a new class of it. By pairing strong cryptography with regional control, minimal retention, rigorous access governance, and transparent compliance, Stratoscan aims to make the trust paradox manageable: share enough to gain clarity, with safeguards that respect how much is at stake.