Skkynet Secures C$2.6M to Speed Secure Industrial AI Development
Skkynet Cloud Systems announced a C$2.6 million project to advance secure Industrial AI, backed by C$1.04 million from the Government of Canada via FedDev Ontario. The news follows a public announcement by the Honourable Evan Solomon, Minister of Artificial Intelligence and Digital Innovation.
The initiative centers on enhancing Skkynet's Cogent DataHub platform to move production-grade data into AI systems safely, then bring AI-driven control logic back to the plant without exposing operational networks.
What the project targets
The focus is simple: build secure, real-time data bridges that let operators stream training data to AI models and run AI-based control actions-while keeping OT networks isolated. This reduces the usual trade-off between data access and security in industrial automation.
The plan includes expanding Skkynet's engineering team and accelerating commercialization for complex production environments. The company expects stronger traction in both domestic and international markets.
"Southern Ontario is home to a thriving tech ecosystem and Skkynet is a great example of the innovation happening right here in our community," said Minister Solomon. "By helping companies like Skkynet scale their AI capabilities, we are strengthening our local economy and securing Canada's place as a leader in the industrial software market."
"With this support from FedDev Ontario, we can expedite our delivery of our leading-edge AI capabilities that will help industries optimize operations, improve efficiency, and compete globally," said Gary Tillery, CEO of Skkynet.
Paul Thomas, President of Skkynet, added that the contribution will fast-track critical developments so industrial clients can adopt AI securely and effectively.
Why this matters for IT, OT, and product teams
Most plants don't struggle to build models-they struggle to move trustworthy data in and out of OT without creating risk. If DataHub delivers on secure bridging, teams can shorten the path from pilot to production and keep network boundaries intact.
- OT-to-ML pipelines: Stream time-series and events to model training without opening risky inbound ports or breaking segmentation.
- Edge inference and closed-loop control: Run AI logic near the process with tight latency windows and clear fallback paths.
- Network isolation: Favor patterns like unidirectional flows, DMZ relays, and explicit allow-lists to protect control systems.
- Data governance: Enforce schema, lineage, tagging, and retention so datasets remain auditable and repeatable.
- MLOps alignment: Integrate model versioning, monitoring, rollback, and drift checks into existing CI/CD and plant change control.
- Compliance posture: Align technical controls with NIST SP 800-82 and IEC 62443 expectations.
Signals to watch from Skkynet
- Security model: Clear inbound/outbound rules, certificate handling, and audit logging for regulated sites.
- Deployment options: Edge, on-prem, and cloud patterns, plus guidance for brownfield environments.
- Interoperability: How bridges integrate with existing historians, data lakes, and monitoring stacks.
- Latency and reliability: Published benchmarks for streaming, inference, failover, and degraded modes.
- APIs and tooling: SDKs, eventing, schema management, and IaC support for repeatable rollouts.
- Commercial model: Licensing, support SLAs, and migration paths for current DataHub users.
Advisors and ecosystem
Skkynet credited Aarif Nakhooda for strategic guidance on the initiative, highlighting his experience in AI frameworks and enterprise strategy. This signals attention to practical adoption-architecture, governance, and the realities of production plants-rather than lab demos.
Where to learn more
Project and product updates: skkynet.ai and company overview: skkynet.com. For context on federal support, see FedDev Ontario's programs and announcements.
If your team is building skills for industrial AI delivery, explore role-based learning paths here: Complete AI Training - Courses by Job.
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