Overview
Aevum Encyclopedia leverages advanced artificial intelligence to index, verify, and connect knowledge across disciplines. Because our AI interacts with sensitive research, historical data, and user queries at scale, security is not an afterthought—it is foundational to our architecture.
This document outlines how we secure our AI systems, protect user data, prevent model misuse, and maintain academic integrity through transparent governance.
Note: All AI-generated content on Aevum is clearly labeled and cross-referenced with primary sources. Our systems never replace expert human verification.
Core Principles
🔐 Zero-Trust Architecture
Every request, internal service, and data flow is authenticated, encrypted, and continuously monitored for anomalies.
🛡️ Adversarial Resilience
Models are stress-tested against prompt injection, data poisoning, and extraction attacks before deployment.
👁️ Human-in-the-Loop
Critical verification steps, citation validation, and sensitive topic reviews require human expert oversight.
⚖️ Bias & Fairness Auditing
Continuous evaluation across demographics, languages, and historical contexts to prevent systemic bias.
Security Architecture
Our AI pipeline is segmented into isolated environments with strict access controls. Data never crosses trust boundaries without explicit validation.
| Layer | Security Measure | Standard |
|---|---|---|
| Ingestion | Sandboxed parsing, content hashing, malware scanning | OWASP AI Security Guidelines |
| Processing | Ephemeral containers, memory isolation, rate limiting | d>ISO/IEC 27017|
| Model Serving | Input sanitization, output filtering, prompt guards | NIST AI RMF |
| Storage | AES-256 encryption, key rotation, geo-sharding | SOC 2 Type II |
Data Privacy
User queries are treated as transient session data. We do not retain identifiable search history for training purposes. Where anonymous telemetry is collected for system optimization, it is aggregated, differential-privacy protected, and fully opt-out.
- Encryption: TLS 1.3 in transit, AES-256-GCM at rest.
- Data Minimization: Only metadata required for indexing and retrieval is stored.
- User Rights: Full GDPR/CCPA compliance. Export, delete, or suspend data processing at any time.
- Third-Party Vetting: All AI infrastructure partners undergo annual security assessments.
Model Safety & Content Moderation
Our AI is fine-tuned for academic and reference use cases. It is explicitly constrained against generating unverified claims, speculative medical/legal advice, or manipulated media.
Guardrails in Action
- Source Binding: Every AI-generated summary must cite verifiable primary sources. Unciteable claims are automatically suppressed.
- Context Window Isolation: User data is never cross-contaminated between sessions or used to modify model weights.
- Red-Teaming: Internal and external security researchers conduct quarterly penetration testing focused on LLM vulnerabilities.
Compliance & Auditing
Aevum Encyclopedia maintains independent third-party audits and publishes annual transparency reports detailing AI performance, safety incidents, and model updates.
📜 Certifications
SOC 2 Type II, ISO 27001, GDPR, CCPA, HIPAA (for health-related reference data where applicable).
🔍 Third-Party Audits
Biannual security reviews by independent firms. AI bias assessments by academic ethics boards.
📢 Transparency Reports
Published quarterly. Covers takedown requests, model versioning, and safety incident metrics.
Frequently Asked Questions
Contact Our Security Team
For vulnerability reports, compliance inquiries, or enterprise security requirements, reach out directly:
Email: security@aevum.com
PGP Key: Download PGP Public Key
Bug Bounty: Program Details