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 In fast-changing threat environments, that model can leave decision-makers working with outdated information.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.
Keep Reading
How a Korean biotech startup is using AI to move drug discovery from trial-and-error to precision design
A close up of a protein structure model. PHOTO: UNSPLASH
For decades, drug discovery has relied on trial and error, with scientists testing thousands of molecules to find one that works. Galux, a South Korean biotech startup, is changing that by using AI to design proteins from scratch. This method, called “de novo” design, makes it possible to build precise new therapies instead of searching through existing ones.
The company recently announced a US$29 million Series B funding round, bringing its total capital to US$47 million.This significant investment attracted a substantial roster of institutional backers, including the Korea Development Bank (KDB), Yuanta Investment, SL Investment and NCORE Ventures. These firms joined existing investors such as InterVest, DAYLI Partners and PATHWAY Investment, as well as new participants including SneakPeek Investments, Korea Investment & Securities and Mirae Asset Securities.
At the core of the company’s work is a platform called GaluxDesign. Unlike many AI tools that only predict how existing proteins fold, this system uses deep learning and physics to create entirely new therapeutic antibodies. This “from scratch” approach lets the team go after so-called “undruggable” proteins. These are targets that traditional small-molecule drugs can’t reach because they lack clear binding pockets. By designing proteins to fit these complex shapes, Galux aims to unlock treatments that have stayed out of reach for decades. And that’s exactly why investors are paying attention.
The pharmaceutical industry is actively looking for faster and more efficient ways to develop new drugs, and Galux is built for exactly that. The company connects its AI platform directly to its own wet lab, where designs can be tested in real time. Each result feeds straight back into the system, sharpening the next round of models. This continuous loop speeds up discovery and improves precision at every step. It’s also why partners like Celltrion, LG Chem and Boehringer Ingelheim are already working with Galux.
Galux is no longer just trying to make drugs that stick to a target. The company now wants its AI to design medicines that actually work in the body and can be made at scale. In simple terms, a drug has to do more than bind to a disease—it must be stable, safe and strong enough to change how the illness behaves. Galux is moving into tougher targets such as ion channels and GPCRs. These play key roles in heart function and sensory signals. Ultimately, the goal is to show that AI-driven design can turn complex biology into real treatments. And instead of hunting blindly for a solution, the team is building exactly what they need.