Securing AI Across Grid Operations, Generation, and Field Service

Utilities, generation owners, and oil and gas operators are deploying AI across grid-operations copilots, predictive maintenance agents for generation and transmission assets, customer billing chatbots, safety and compliance reporting automation, and field-service dispatch. Each new agent sits closer to SCADA and OT systems than prior software generations, widening an attack surface reliability coordinators are only beginning to measure.

PointGuard AI gives every agent a cryptographic identity, validates each action against policy before execution, and contains rogue behavior in real time, keeping AI a decision-support tool, not an autonomous controller, as NERC requires for grid-adjacent systems. Continuous testing, AI-native data leak prevention, and tamper-evident audit trails map findings to NERC CIP, NIST AI RMF, MITRE ATLAS, and OWASP Agentic and LLM Top 10. As adversaries weaponize AI faster than defenders can respond, utilities, generation, and energy operators need continuous visibility and control over every agent touching the grid.
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Energy Industry Use Cases

Continuous Testing of Grid AI Systems

Grid-operations copilots and predictive-maintenance agents change every sprint, while attackers iterate weekly on new injection and jailbreak techniques. A one-time assessment at launch cannot catch an exploit discovered months later.

PointGuard runs automated, continuous adversarial testing against grid copilots and agents, refreshing coverage as models and prompts change.

Pre-Execution Validation for Every Grid Agent

An agent advising on switching or dispatch can issue an irreversible command in milliseconds. Static role-based access cannot anticipate every way an agent might combine actions near SCADA and OT boundaries.

PointGuard validates every proposed action against policy and trust score at sub-millisecond latency, blocking unsafe steps before execution.

Stopping Data Poisoning of Grid Models

Predictive-maintenance and voltage-monitoring models trained on manipulated data can misread sensor input and issue unsafe recommendations. Keyword filters do not recognize adaptive, encoded poisoning attempts hidden in training data or retrieved documents.

PointGuard's fine-tuned small language models detect prompt injection and data-poisoning patterns at sub-100ms latency, mapped to OWASP.

Full Observability for Every OT-Adjacent Agent

Agents supporting generation and transmission operations make hundreds of decisions per second across tool calls and handoffs. Without dedicated telemetry, teams reconstruct incidents from fragments, and audit gaps undermine response and compliance.

PointGuard captures OpenTelemetry-compatible, tamper-evident traces for every agent action and tool call, giving forensic reconstruction and audit-ready evidence.

Compliance Evidence Mapped to NERC CIP

Safety and compliance reporting increasingly runs through AI, yet no framework tells auditors which models meet NERC CIP or NIST AI RMF baselines, and hand-mapped evidence does not scale across the fleet.

PointGuard maps every finding to NERC CIP, NIST AI RMF, MITRE ATLAS, and ISO/IEC 42001 with tamper-evident evidence.
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Shadow AI Discovery in Customer Operations

Billing and service teams adopt outside AI tools and MCP-connected assistants faster than security can review them, often to close an AI skills gap. Each unsanctioned tool touches customer accounts unassessed.

PointGuard runs continuous, multi-vector discovery across clouds and runtime telemetry, surfacing unsanctioned AI tools within minutes.

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