Machine Learning (ML/AI) Ecosystem Security

Ensure your AI/ML implementations start off on the right foot — and stay that way.

AIOps is the buzzword — AI for IT operations. But what about its cousin, IT operations for AI?

A production-grade, fully functional ecosystem to support the use of AI/ML in your enterprise environment requires, among other things, a number of supporting subsystems:

  • Data collection and ingestion

  • Orchestration engine

  • Auto scale-up/scale-down

  • ETL

  • API gateway(s)

  • Configuration management

  • Cybersecurity protection (IDS, IPS, IAM, event logging, certificate management, authentication, etc.)

  • Server infrastructure (cloud, on-premise, or both)

  • Application platform scaffolding

  • Network/SDN management (VPN tunnels, routing, bandwidth management, etc.)

  • Monitoring

  • Data validation and verification

Without these subsystems, the core AI/ML engine (even in an IaaS/PaaS/SaaS) environment cannot exist — not to mention thrive — in a production environment.

Effective management of this collection of systems and services involves skillful instrumentation and lightning-fast root cause analysis when things go awry. Rule4 has the unique combination of experience and skills to span the breadth of this complex landscape.

Build your ML/AI house on a solid foundation!

Let's discuss your organization’s ML/AI ecosystem security challenges and opportunities.