Your engineers are already using ChatGPT to draft SOPs. Your traders might be feeding market position data into Claude. Your operations team is uploading maintenance logs to AI summarizers they found on Product Hunt. Your compliance team doesn't know about any of it.
Welcome to shadow AI in the power sector.
What Is Shadow AI?
Shadow AI is the use of artificial intelligence tools by employees without formal IT approval, security review, or governance oversight. It's the AI equivalent of shadow IT, but with a critical difference: AI tools don't just process data, they learn from it.
When an engineer pastes a proprietary turbine fault code into ChatGPT, that data enters a system they don't control. When a trader asks AI to "analyze this P&L spreadsheet," sensitive financial data leaves the corporate perimeter. When someone uploads SCADA screenshots to an image-recognition tool to identify anomalies, operational technology data is now in the hands of a third party.
In most industries, this is a data privacy concern. In the power sector, it's a potential NERC CIP violation, a market manipulation risk, and a grid security issue all at once.
Why Shadow AI Is Accelerating in Energy
Three forces are driving Shadow AI adoption faster than governance can keep up:
1. The productivity gap is real. Engineers using AI tools report 30–50% time savings on documentation, analysis, and troubleshooting. When your team is understaffed and facing regulatory deadlines, the temptation to use any available tool is enormous. Furthermore, the stakes for work output being right have never been higher. When jobs (read: the families behind those jobs) are perceived to be at risk due to the doom-zeitgeist of the day, folks are willing to risk shadow usage to ensure their output is of the highest quality relative to their peers. Research has shown that workers who utilize AI tools in their workflows produce output that is more correct. Once your standard of correctness has risen through the shadow use of AI, it's hard to go back to antiquated forms of work.
2. AI tools are consumer-grade accessible. Unlike enterprise software that requires procurement, AI tools are a browser tab and a few CLI strokes away. No purchase order. No IT ticket. No approval chain. Just a personal email and a credit card.
3. Corporate AI strategy is moving too slowly. When the official answer is "we're evaluating AI" for the third consecutive quarter, employees stop waiting. They solve their own problems. Can you blame them? Counterintuitively enough, if your organization is moving slowly on AI implementation and you're noticing a rise in shadow AI usage across the organization, it's a sign that you've hired the right people — ones who take measured risk to ensure their output is the best, their speed to ship is unbelievable, and their productivity is better than your competitors.
The Power Sector's Unique Risk Profile
Shadow AI creates risks in any industry, but the power sector faces a particularly dangerous combination:
NERC CIP Compliance Exposure
NERC CIP standards govern the security of the bulk electric system. If employees are feeding BES Cyber System information into unauthorized AI tools, you may be in violation of CIP-004 (Personnel & Training), CIP-011 (Information Protection), and potentially CIP-003 (Security Management Controls). These aren't theoretical concerns — NERC auditors are already asking about AI use in their compliance assessments.
Market Data Leakage
In competitive wholesale power markets, bid strategies, fuel cost data, unit commitment plans, and congestion analysis are commercially sensitive. If traders or analysts are using AI tools to process this data, even inadvertently, it could constitute information sharing that runs afoul of FERC market behavior rules.
Operational Technology (OT) Risks
The convergence of IT and OT in modern power systems means SCADA data, DCS configurations, relay settings, and control system architectures could end up in AI prompts. This is both a data loss concern and a potential attack surface expansion. If AI tools are trained on or retain this data, you've effectively given an external party a blueprint for your operational technology environment.
Environmental and Safety Reporting
EPA, OSHA, and state regulatory filings contain sensitive operational data. Using AI to draft or analyze these reports without proper data handling creates liability exposure. If an AI hallucination makes it into a regulatory filing because someone used ChatGPT to "help with the wording," the regulatory consequences are real and measurable.
A Five-Step Shadow AI Response Framework
Banning AI outright doesn't work and will just drive shadow AI deeper underground. Instead, energy companies need a pragmatic framework that acknowledges the productivity benefits while managing the risks.
Step 1: Discover What's Already Happening
Before you can manage shadow AI, you need to know where it exists. Run a confidential survey. Check network logs for traffic to AI services. Talk to team leads. The goal isn't to punish shadow users, but to understand the scope, the need, and the productivity advantages that led to shadow AI. In our experience, companies are consistently surprised by how widespread AI use already is.
Step 2: Classify Your Data and Workflows
Not all data or workflows carry the same risk. Create a simple classification that maps to AI usage rules: public data (free to use with any AI), internal workflows (approved AI tools only), confidential data (enterprise AI with data protection agreements only), and restricted data (no AI processing without explicit CISO approval). This gives employees a clear framework instead of a blanket ban.
Step 3: Provide Sanctioned Alternatives
This is where most companies fail. They say "don't use Claude" but don't provide anything in its place. Deploy enterprise AI tools with proper data handling. Bring forth AI solutions where your data stays within your security perimeter, where you control retention policies, where usage is auditable, and that your teams actually want to use. If the sanctioned tool is even 80% as good as the shadow AI alternative, most employees will switch.
Step 4: Build Lightweight Governance
Your AI governance framework doesn't need to be a 200-page policy document. Start with a one-page acceptable use policy that defines what data can go into which AI tools, establishes a simple review process for new AI tool requests, and assigns an AI governance owner. You can iterate and expand on this later, but you need something in place now. Again, counterintuitively, one of your top shadow AI users might be the best person to lead such an AI governance initiative.
Step 5: Monitor and Iterate
Shadow AI isn't a problem you solve once, it's an ongoing management challenge. There are a number of ways to monitor and manage AI usage: implement network monitoring for unauthorized AI tool usage, run quarterly reviews of your AI tool inventory, and keeping your data classification updated as new AI capabilities emerge are just a few to name here. And keep talking to your employees — they'll tell you what's not working if you create a safe space to do so. It also helps to have an amazing external partner, like Brightwire, to help monitor usage, manage costs and models, train employees on tooling, and keep the architecture updated.
The Real Risk Is Inaction
The companies that will face the worst shadow AI consequences aren't the ones using AI aggressively. It is the companies that pretend AI isn't happening and failing to respond to the way organizations are changing because of AI. Every month you delay a coherent AI strategy, your employees are making individual decisions about data handling, tool selection, and security trade-offs that should be organizational decisions.
Shadow AI is a symptom. The disease is the gap between AI demand and AI governance. Close that gap with sanctioned, secure AI tools and clear policies. If you do this well, you transform an AI risk into a competitive advantage. The power sector doesn't have the luxury of figuring this out slowly. The data is too sensitive, the regulatory environment too strict, and the consequences of a breach too severe.
Start with discovery. Start with Brightwire.
How Brightwire Can Help
At Brightwire.ai, we help energy companies move from shadow AI chaos to managed AI integration. Our approach starts with Phase 1: secure, read-only enterprise AI tools that give your teams the productivity gains they're seeking, without the compliance risks they're currently taking. No autonomous agents. No black boxes. Just pragmatic AI adoption that your CISO, your compliance team, and your board can all get behind.
Ready to get ahead of shadow AI? Let's talk.