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
A person tightrope walking between two cliffs. PHOTO: UNSPLASH
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 In fast-changing threat environments, that model can leave decision-makers working with outdated information.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.
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Why investors are backing Applied Brain Research’s on-device voice AI approach.
Updated
January 28, 2026 5:53 PM
Plastic model of a human's brain. PHOTO: UNSPLASH
Applied Brain Research (ABR), a Canada-based startup, has closed its seed funding round to advance its work in “on-device voice AI”. The round was led by Two Small Fish Ventures, with its general partner Eva Lau joining ABR’s board, reflecting investor confidence in the company’s technical direction and market focus.
The round was oversubscribed, meaning more investors wanted to participate than the company had planned for. That response reflects growing interest in technologies that reduce reliance on cloud-based AI systems.
ABR is focused on a clear problem in voice-enabled products today. Most voice features depend on cloud servers to process speech, which can cause delays, increase costs, raise privacy concerns and limit performance on devices with small batteries or limited computing power.
ABR’s approach is built around keeping voice AI fully on-device. Instead of relying on cloud connectivity, its technology allows devices to process speech locally, enabling faster responses and more predictable performance while reducing data exposure.
Central to this approach is the company’s TSP1 chip, a processor designed specifically for handling time-based data such as speech. Built for real-time voice processing at the edge, TSP1 allows tasks like speech recognition and text-to-speech to run on smaller, power-constrained devices.
This specialization is particularly relevant as voice interfaces become more common across emerging products. Many edge devices such as wearables or mobile robotics cannot support traditional voice AI systems without compromising battery life or responsiveness. The TSP1 addresses this limitation by enabling these capabilities at significantly lower power levels than conventional alternatives. According to the company, full speech-to-text and text-to-speech can run at under 30 milliwatts of power, which is roughly 10 to 100 times lower than many existing alternatives. This level of efficiency makes advanced voice interaction feasible on devices where power consumption has long been a limiting factor.
That efficiency makes the technology applicable across a wide range of use cases. In augmented reality glasses, it supports responsive, hands-free voice control. In robotics, it enables real-time voice interaction without cloud latency or ongoing service costs. For wearables, it expands voice functionality without severely impacting battery life. In medical devices, it allows on-device inference while keeping sensitive data local. And in automotive systems, it enables consistent voice experiences regardless of network availability.
For investors, this combination of timing and technology is what stands out. Voice interfaces are becoming more common, while reliance on cloud infrastructure is increasingly seen as a limitation rather than a strength. ABR sits at the intersection of those two shifts.
With fresh funding in place, ABR is now working with partners across AR, robotics, healthcare, automotive and wearables to bring that future closer. For startup watchers, it’s a reminder that some of the most meaningful AI advances aren’t about bigger models but about making intelligence fit where it actually needs to live.