1. Use-case taxonomy, risk tiering and standards alignment
Organize AI applications by function and risk, and relate them to existing standards and governance expectations so teams can prioritize where deeper testing and controls are needed.
Electric-sector AI risk · Independent discussion
SAFERai.power: Operational AI Risk for the Electric Sector
An independent engineering perspective on EPRI’s emerging framework for evaluating, testing and monitoring AI applications used by energy companies.
Overview
What SAFERai.power is
SAFERai.power is an EPRI-led initiative aimed at building an operational risk layer for AI used in the electric sector — including risk framing, evidence templates, and assessment tools that help organizations evaluate AI before and during deployment in utility workflows.
Published project description: EPRI Product ID 3002036393, June 2026. The document describes a 24-month leadership-coalition effort and an OPAI-aligned operational risk framework with an open-source assessment toolkit for energy companies.
Public materials describe SAFERai.power as complementary to broader AI governance concepts: it focuses on sector-specific evidence, testing, monitoring, and guardrails for power-system use cases, rather than inventing a brand-new general AI governance model. Organizations should confirm current scope and deliverables on the official EPRI site.
This is an independent discussion of publicly available EPRI information. General Reliability is not affiliated with or endorsed by EPRI. SAFERai.power does not replace applicable NERC requirements or an organization’s legal, cybersecurity, engineering or compliance responsibilities.
Why this matters
Why the electric sector needs an operational AI-risk framework
Energy companies are exploring AI for planning support, operations aids, asset analytics, customer workflows, and documentation tasks. In a reliability-critical sector, AI failures are not only software bugs — they can affect safety, operating decisions, evidence quality, and trust.
An operational framework helps teams ask practical questions: What can go wrong? How autonomous is the system? What evidence is required before use? What monitoring and human review stay in place after deployment?
Engineering focus
- Failure modes and consequence thinking
- Use-case risk tiers and autonomy limits
- Evidence before trust
- Monitoring and incident triggers
- Human review that remains accountable
Initiative structure
Four workstreams
The following workstream framing is presented for engineering discussion of the emerging initiative. Confirm details, sequencing, and deliverables against EPRI’s official SAFERai materials.
2. Risk protocols and playbooks
Define practical protocols for identifying failure modes, setting autonomy limits, documenting guardrails, and deciding when a use case is ready for limited or broader deployment.
3. Pilot validation and evidence calibration
Use pilots to test whether evidence packages, risk ratings, and review steps work in real utility contexts — and calibrate expectations before scaling.
4. Toolkit and adoption infrastructure
Build shared assessment tooling and adoption support so organizations can apply consistent methods across providers, deployers, and use cases.
Evidence packages
AI System Risk File
Public descriptions of SAFERai.power emphasize evidence packages and AI system risk file templates for provider and deployer evidence. In engineering terms, a risk file is a living package that documents what the AI does, where it is used, what can fail, what tests were run, and what controls remain after deployment.
What belongs in a risk file
Use-case description, data sensitivity, autonomy level, known failure modes, test results, guardrails, monitoring plan, and human-review ownership.
Why it matters
Without a durable evidence package, organizations cannot explain why an AI tool was accepted, what limits were set, or how incidents will be handled.
GR connection
GR helps customers turn abstract AI risk language into reviewable engineering evidence — without claiming that any template automatically satisfies compliance.
TEVV
Testing, evaluation, verification and validation
Operational AI risk management depends on testing that matches the use case: accuracy under realistic inputs, behavior under incomplete or adversarial conditions, escalation paths, and whether outputs remain within approved boundaries.
Verification and validation should be scoped to the decision impact of the AI system. Higher-consequence power-system uses need stronger evidence and clearer human-in-the-loop controls than low-risk drafting aids.
Practical TEVV questions
- What failure would create an operating or safety consequence?
- What inputs and edge cases were tested?
- Who reviews outputs before action?
- What evidence is retained after deployment?
Operations after deployment
Guardrails, monitoring, incident triggers and evidence
Guardrails
Limits on autonomy, allowed data, approved actions, and required human approval before consequential steps.
Monitoring & incident triggers
Signals that an AI system is drifting, producing unsafe suggestions, escalating incorrectly, or operating outside its approved envelope.
Evidence continuity
Records that show what was tested, what was approved, what was monitored, and how incidents were handled over time.
Standards context
Relationship to NERC requirements
SAFERai.power is discussed here as an operational AI-risk initiative for the electric sector. It may help organizations organize evidence and testing related to AI use, but it does not replace applicable NERC requirements, CIP programs, reliability standards, or legal compliance obligations.
For GR’s separate NERC & grid reliability change watch — focused on emerging reliability requirements, not AI frameworks — see NERC & Grid Reliability Change Watch. Keep the two subjects distinct when planning studies, CIP work, or AI adoption.
How GR can help
From AI failure modes to power-system consequences
General Reliability helps organizations translate AI failure modes into power-system consequences, engineering tests, acceptance criteria, and human-review controls — so AI remains decision support, not an uncontrolled operating agent.
Consequence mapping
Identify where AI outputs could affect planning recommendations, operating guidance, customer communications, or documentation quality.
Engineering tests & acceptance criteria
Define practical tests and pass/fail expectations before a use case is trusted with sensitive data or consequential workflows.
Human-review controls
Design review checkpoints, escalation rules, and evidence packages that keep people accountable for final decisions.
AI Reliability & Trust services → · Reliable AI Automation → · Secure local AI →
Primary source
Official EPRI SAFERai materials
Use the official EPRI SAFERai micro site as the primary public reference for initiative scope and updates:
https://msites.epri.com/saferai
If EPRI publishes additional project pages or documents, confirm them on epri.com / EPRI microsites before citing them as authoritative.
Discuss operational AI risk for your organization
Contact GR to discuss AI screening, evidence packaging, testing expectations, and human-review controls for energy and utility workflows.