What Overstory’s vegetation intelligence reveals about wildfire and outage risk.
Updated
January 15, 2026 8:03 PM
Aerial photograph of a green field. PHOTO: UNSPLASH
Managing vegetation around power lines has long been one of the biggest operational challenges for utilities. A single tree growing too close to electrical infrastructure can trigger outages or, in the worst cases, spark fires. With vast service territories, shifting weather patterns and limited visibility into changing landscape conditions, utilities often rely on inspections and broad wildfire-risk maps that provide only partial insight into where the most serious threats actually are.
Overstory, a company specializing in AI-powered vegetation intelligence, addresses this visibility gap with a platform that uses high-resolution satellite imagery and machine-learning models to interpret vegetation conditions in detail.Instead of assessing risk by region, terrain type or outdated maps, the system evaluates conditions tree by tree. This helps utilities identify precisely where hazards exist and which areas demand immediate intervention—critical in regions where small variations in vegetation density, fuel type or moisture levels can influence how quickly a spark might spread.
At the core of this technology is Overstory’s proprietary Fuel Detection Model, designed to identify vegetation most likely to ignite or accelerate wildfire spread. Unlike broad, publicly available fire-risk maps, the model analyzes the specific fuel conditions surrounding electrical infrastructure. By pinpointing exact locations where certain fuel types or densities create elevated risk, utilities can plan targeted wildfire-mitigation work rather than relying on sweeping, resource-heavy maintenance cycles.
This data-driven approach is reshaping how utilities structure vegetation-management programs. Having visibility into where risks are concentrated—and which trees or areas pose the highest threat—allows teams to prioritize work based on measurable evidence. For many utilities, this shift supports more efficient crew deployment, reduces unnecessary trims and builds clearer justification for preventive action. It also offers a path to strengthening grid reliability without expanding operational budgets.
Overstory’s recent US$43 million Series B funding round, led by Blume Equity with support from Energy Impact Partners and existing investors, reflects growing interest in AI tools that translate environmental data into actionable wildfire-prevention intelligence. The investment will support further development of Overstory’s risk models and help expand access to its vegetation-intelligence platform.
Yet the company’s focus remains consistent: giving utilities sharper, real-time visibility into the landscapes they manage. By converting satellite observations into clear and actionable insights, Overstory’s AI system provides a more informed foundation for decisions that impact grid safety and community resilience. In an environment where a single missed hazard can have far-reaching consequences, early and precise detection has become an essential tool for preventing wildfires before they start.
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Quantara AI launches a continuous platform designed to estimate the financial impact of cyber risk as companies move beyond periodic assessments
Updated
February 20, 2026 6:43 PM
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Cyber risk is increasingly treated as a financial issue. Boards want to know how much a cyber incident could cost the company, how it could affect earnings, and whether current security spending is justified.
Yet many organizations still measure cyber risk through periodic reviews. These assessments are often conducted once or twice a year, supported by consultants and spreadsheet models. By the time the report reaches senior leadership, the company’s systems may have changed and new threats may have emerged. The way risk is measured does not always match how quickly it evolves.
This gap is where Quantara AI is positioning its new platform. Quantara AI, a Boise-based cybersecurity startup, has introduced what it describes as the industry’s first persistent AI-powered cyber risk solution. The system is designed to run continuously rather than rely on occasional assessments.
The company’s core argument is straightforward: not every security weakness carries the same financial consequence. Instead of ranking issues only by technical severity, the platform analyzes active threats, identifies which company systems are exposed, and estimates how much money a successful attack could cost. It uses statistical models, including Value at Risk (VaR), to calculate potential losses. It also estimates how specific security improvements could reduce that projected loss.
The timing aligns with a broader market shift. International Data Corporation (IDC) projects that by 2028, 40% of enterprises will adopt AI-based cyber risk quantification platforms. These tools convert security data into financial estimates that can guide budgeting and investment decisions. The forecast reflects growing pressure on security leaders to present risk in terms that boards and regulators understand.
Traditional compliance and risk management systems often focus on meeting regulatory standards. Vulnerability management programs typically score weaknesses based on technical characteristics. Consultant-led risk studies provide detailed analysis, but they are usually performed at set intervals. In fast-changing threat environments, that model can leave decision-makers working with outdated information.
Quantara’s platform attempts to replace that periodic process with continuous measurement. It brings together threat data, internal system information and financial modeling in one system. The goal is to show, at any given time, which specific weaknesses could lead to the largest financial losses.
Cyber risk quantification as a concept is not new. What is changing is the expectation that these calculations be updated regularly and tied directly to financial decision-making. As cyber incidents carry clearer monetary consequences, companies are looking for ways to measure exposure with greater precision.
The broader question is whether enterprises will shift fully toward continuous, AI-driven risk analysis or continue relying on periodic external assessments. What is clear is that cybersecurity discussions are moving closer to financial reporting — and tools that estimate potential loss in dollar terms are becoming central to that shift.