Quantara AI launches a continuous platform designed to estimate the financial impact of cyber risk as companies move beyond periodic assessments
Updated
March 17, 2026 1:02 AM
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 In fast-changing threat environments, that model can leave decision-makers working with outdated information.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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Sonilo and Shutterstock are betting that licensed training data could define the future of AI music.
Updated
May 13, 2026 3:39 PM
A human operating a digital turntable. PHOTO: UNSPLASH
As copyright disputes continue to grow around AI-generated music, Sonilo, the world’s first professionally licensed video-to-music AI platform, has partnered with Shutterstock to train its models on licensed music catalogs.
The agreement gives Sonilo access to Shutterstock’s music library for AI model training. According to the companies, it is Shutterstock’s first partnership with a video-to-music AI platform and the timing is significant. AI music companies are facing growing pressure over how their systems are trained. Artists and record labels have increasingly challenged the use of copyrighted music in AI datasets, especially when licensing agreements or compensation structures are unclear.
That tension has created a divide across the industry. Some companies have continued building models around scraped or disputed data. Others are trying to position licensing as part of the product itself.
Sonilo falls into the second group. The company says its models are trained only on licensed material where artists and rights holders have agreed to participate and receive compensation. The Shutterstock partnership strengthens that position while giving Sonilo access to a larger pool of commercially cleared music.
The collaboration also points to a broader change happening inside generative AI. As AI tools move into commercial production, companies are being pushed to show not just what their models can generate, but also where their training data comes from.
Sonilo’s platform is built around video rather than text prompts. The system analyses footage directly, studies pacing and emotional tone, then generates an original soundtrack to match the content. The company says this removes the need for manual music searches, syncing or editing workflows. The generated tracks are cleared for commercial use across social media, branded content and broadcast production.
Shawn Song, CEO of Sonilo, said: "Music has always been the last unsolved layer of video creation, and video has always carried its own soundtrack. We built Sonilo to hear it and compose from it, without a single text prompt. But how we build matters as much as what we build. While others have chosen to take artists' work without permission and charge creators for the privilege, we've chosen a different path—one where artists are compensated from day one. Partnering with Shutterstock reflects that standard. Every model we train meets a bar the music industry can stand behind, because the most innovative AI platforms don't have to come at the expense of the artists who make all of these possible."
For Shutterstock, the deal expands the company’s growing role in generative AI infrastructure. The company has increasingly focused on licensing content for AI systems across images, video and music.
Jessica April, Vice President of Data Licensing & AI Services at Shutterstock, said: "AI innovation depends on access to high-quality, rights-cleared content and trusted licensing partnerships. Sonilo's approach reflects the growing demand for responsibly sourced training data and commercially safe AI workflows. We're pleased to support companies building generative AI products with licensed content and scalable data solutions that help accelerate innovation while respecting creators and rights holders."
The partnership also comes as Sonilo expands into creator and developer ecosystems. Earlier this month, the company launched as a native node inside ComfyUI, an open-source AI workflow platform used by millions of creators. Sonilo also offers API access for integration into creator tools, video platforms, game engines and other AI systems.
As AI-generated music becomes more common across advertising, creator platforms and digital media, the industry’s focus is shifting beyond generation alone. Questions around licensing, ownership and compensation are increasingly shaping how AI music companies position themselves and build trust with creators.