AI Systems Are High-Value Targets
Enterprise AI platforms process emails, financial data, contracts, and personal information. They connect to dozens of external services and execute automated actions. This makes them an attractive attack surface — a compromised AI agent with access to your CRM, email, and financial systems can cause more damage than a traditional data breach. Yet many AI deployments treat security as an afterthought, bolting it on after the core system is built.
At ESKOM.AI, security is built into the architecture of our AI platform from day one. Every layer — from network access to individual agent permissions — follows defense-in-depth principles. Here's how we approach each layer.
Network and Infrastructure
All platform services communicate over a private VPN with end-to-end encryption. No service is exposed to the public internet directly. Inter-service communication uses private IPs (100.x.x.x range), and external access is gated through a reverse proxy with IP allowlisting. The infrastructure runs on dedicated hardware — no shared cloud instances where noisy neighbors could enable side-channel attacks.
Every file uploaded to or generated by the system passes through antivirus scanning before it enters the processing pipeline. This catches malware-laden attachments in emails, infected documents from external integrations, and potentially malicious payloads in API requests. It's a basic measure, but one that many AI platforms skip entirely.
Data Protection and GDPR Compliance
Processing personal data through AI models creates GDPR exposure. Our solution is Anoxy — a dedicated PII anonymization service that intercepts data before it reaches any LLM. Anoxy detects and masks personal identifiers (names, emails, phone numbers, PESEL numbers, addresses) in real-time, replacing them with reversible tokens. The LLM processes anonymized data, and the original values are restored only in the final output, visible only to authorized users.
- Automatic PII detection across 15+ entity types
- Reversible tokenization — anonymize for processing, de-anonymize for output
- Audit logging — every anonymization event is recorded with timestamp, entity type, and requesting agent
- Configurable sensitivity — different anonymization levels per agent and per data category
Application Security and Audit
Our platform follows OWASP Top 10 v3 guidelines across all API endpoints. This includes input validation, output encoding, authentication via enterprise SSO with secure authorization, role-based access control (RBAC), and rate limiting. Each agent operates under the principle of least privilege — an HR agent cannot access financial data, and a DevOps agent cannot read executive emails.
Every action in the system generates an immutable audit trail: which agent performed the action, what data was accessed, which LLM was used, and what output was produced. This isn't just for compliance — it's essential for debugging, quality assurance, and accountability. When an agent makes a decision, you can trace the entire reasoning chain back to the original input. For enterprises evaluating AI platforms, our recommendation is simple: if a vendor can't explain their security model in detail, they probably don't have one.