The convergence of private wireless networks and artificial intelligence in utility operations represents one of the most significant technology shifts the sector has faced in decades. Private LTE and 5G networks are connecting substations, remote assets, field crews, and distributed energy resources with a reliability and security architecture that public mobile networks cannot match. AI systems are beginning to operate on that data in real time — predicting equipment failures, managing distributed energy resources, optimising field workforce dispatch, and detecting grid anomalies.
But this convergence creates a new and substantial cybersecurity challenge. Each new connected device, each AI system with network access, each edge compute node at a substation or remote site extends the attack surface of an operational technology environment that was not designed with modern threat actors in mind. The consequences of a successful cyberattack on utility OT infrastructure — from data exfiltration to operational disruption to physical equipment damage — are among the most serious in any sector.
The TeckNexus × Palo Alto Networks Utility Private Network Security Assessment provides a structured, five-domain evaluation of security posture across the full technology stack — from the private network core to edge computing, AI ecosystem, and governance frameworks — and produces a prioritised remediation roadmap aligned to NERC CIP, IEC 62443, and IEC 61850 compliance requirements.
Why Utility OT Security Is Uniquely Challenging
Utilities face a security challenge that is fundamentally different from enterprise IT security. The OT environment — protection relays, RTUs, SCADA systems, DCS platforms — was designed for reliability and determinism, not for cybersecurity. Many of these systems run proprietary protocols on hardware with lifecycles measured in decades, cannot be patched without operational disruption, and were never intended to be network-connected at all.
The private network changes this equation in a fundamental way: it connects the OT environment to IP-based systems, creating pathways between the operational network and systems that can communicate with the outside world. Without careful security architecture, these pathways become attack vectors. The SolarWinds and Colonial Pipeline incidents — neither of which involved utilities’ core OT directly — demonstrated how IT-OT convergence without adequate security architecture can have operational consequences that reach far beyond the compromised system.
At the same time, the AI systems now being deployed on utility private networks introduce a new category of risk. AI models that influence operational decisions — predictive maintenance recommendations, demand forecasting inputs, DER management outputs — can be targeted through adversarial attacks that manipulate model inputs or poison training data. The security architecture must protect not just the network and devices, but the integrity of the AI systems operating on that network.
The Five Security Domains
The assessment covers five domains that together encompass the full security architecture of a utility private network with AI capabilities.
- Domain 1 — Network Foundation: The foundation layer covers the security architecture of the private network core itself: spectrum isolation and interference protection, core network segmentation, management plane security, and the authentication and access control framework. For utilities, this includes the security of the spectrum management system and the network management platform — systems that, if compromised, could affect network availability across multiple sites.
- Domain 2 — Threat and Perimeter: The threat and perimeter layer addresses how threats are detected and contained across the OT-IT boundary and the private network edge. This includes OT-aware segmentation between IT and OT network segments, north-south and east-west traffic inspection, intrusion detection systems capable of recognising industrial protocol anomalies, and the security controls at the interface between the private network and external systems.
- Domain 3 — AI and Edge Security: The AI and edge security domain is specific to this tool and reflects the operational reality that AI systems and edge compute nodes are now part of the utility security perimeter. This domain covers the security controls for edge compute platforms at substations and remote sites, the integrity protection of AI model inputs and outputs, the access controls for AI systems that influence operational decisions, and the monitoring of AI system behaviour for anomalies that might indicate adversarial manipulation.
- Domain 4 — Architecture and Resilience: The architecture and resilience domain assesses the network’s ability to maintain security posture under failure conditions and active attack. Zero trust architecture implementation across the private network, the security of handover and roaming configurations, network slice isolation for critical versus non-critical traffic, and the security implications of edge caching and content delivery are all evaluated in this domain.
- Domain 5 — Governance and Compliance: The governance and compliance domain addresses the regulatory and process dimensions of utility private network security: NERC CIP compliance status, IEC 62443 implementation maturity, incident response capability, vulnerability management processes, and the governance framework for third-party access to the private network and connected OT systems.
How the Assessment Works
The assessment takes approximately 20–25 minutes and is designed for two audience types: utility decision-makers who need an executive-level view of security posture across all five domains, and utility security architects or network engineers who need a detailed technical assessment with specific remediation guidance.
Users select their audience type at the start of the assessment, which determines the depth and technical specificity of the questions. The same scoring framework applies to both — the output report adapts its language and level of detail to the audience.
Questions are structured in sections corresponding to the five domains. Each section uses a mix of single-select, multi-select, and slider inputs to capture both the presence of security controls and the maturity level of their implementation. The scoring model weights questions by their regulatory significance and operational risk impact — a gap in NERC CIP critical infrastructure protection controls scores more heavily than a gap in non-mandatory best practice.
The Output: Prioritised Remediation Roadmap
The assessment output provides a section-by-section security score across the five domains, a tier classification (Foundations, Developing, Capable, or Advanced), a prioritised list of remediation actions ranked by regulatory significance and operational risk reduction, and a compliance gap analysis mapped to NERC CIP, IEC 62443, and IEC 61850 requirements.
Critically, the remediation roadmap distinguishes between actions that address critical compliance gaps — where the risk of inaction is regulatory or operational — and actions that represent best-practice improvements where the current posture is acceptable but not optimal. This distinction allows security teams to make an evidence-based case for prioritised investment without the false urgency of treating all gaps as equally critical.
The assessment is produced in partnership with Palo Alto Networks, whose zero trust and OT security capabilities are directly relevant to the remediation pathways identified for each domain. Following the assessment, users have the option to connect with Palo Alto Networks for a no-cost architecture consultation based on their specific results.
Security as the Foundation for Utility AI
One of the consistent findings from utility private network deployments is that security architecture decisions made during initial network design are extremely difficult and expensive to retrofit later. A network designed without adequate OT segmentation from the outset, or without a zero trust access control framework, requires significant re-engineering to secure properly once the operational environment is live and dependent on the network.
This is why the Security Assessment is positioned as a foundational tool — one that should be used before or alongside the AI Use Case Prioritiser and ROI Calculator, rather than after deployment. Understanding your current security posture and the gaps that need to be addressed shapes both the AI use case selection (some AI applications cannot be deployed safely on an inadequately secured network) and the cost model (security remediation is a real capital cost that belongs in the investment case).
