Transform 2021

Elevate your enterprise data technology and strategy.

July 12-16

Noname Security, a cybersecurity platform that allows enterprises to manage APIs, today closed a $60 million series B funding round led by Insight Partners, with Next47, Forgepoint, TSG, Cyberstarts, and Lightspeed Venture Partners participating. It brings the company’s total raised to $85 million and will be used to scale Noname’s go-to-market and customer success efforts as well as its product and R&D teams.

APIs, the connectors that clouds and apps use to communicate with each other, will become the cyberattacker’s target of choice, according to Gartner. The analyst firm predicts that by 2022, API attacks will be the most frequent attack vector across the enterprise. API vulnerabilities can take many forms, from a developer’s forgotten side project to a software interface improperly configured. While some of these flaws are documented, the vast majority go unnoticed, giving anyone who can find them access to an organization’s operations.

Noname’s platform seeks to address this by automatically discovering, remediating, and testing various APIs. It analyzes API call flows, detecting problems and anomalous behaviors even in the absence of an outside threat, like an API marked “internal” that’s exposed to the open web. Noname also identifies potential issues during development and testing, so that vulnerabilities don’t go live. And it blocks attacks in real time and integrates with workflows to resolve vulnerabilities and erroneous setups.

“I cofounded Noname in 2020 with our CTO, Shay Levi. We served together in Unit 8200 of the Israeli Defense Forces, which is sometimes referred to as the ‘startup factory,'” Noname CEO Oz Golan told VentureBeat via email. “When we first started the company, for almost a year, we met chief information security officers from some of the largest companies in the world to learn about their biggest challenges, and API security was the number one issue … We saw an opportunity to create a single platform to address all security vulnerabilities for all APIs.”

API protection

APIs continue to be an important tool for software developers and companies in general. Enterprises of all sizes from a wide range of industries continue to rely on APIs, and most plan to expand their API usage in the upcoming year. In fact, almost 67% expected to use APIs more in 2020 compared to 2019, according to a recent RapidAPI survey.

“[There’s been a] massive upward trend in API usage as companies continue to invest in making services and assets available through digital transformation initiatives,” RapidAPI CEO Iddo Gino said in a statement. “As the survey data suggests, this trend is present across all industries and API usage increases as a company’s software development team begins to expand.”

Noname trains a machine learning model for each API based on its real-time usage, leveraging unsupervised learning techniques. Where labeled datasets don’t exist, unsupervised models help to fill in the gaps in domain knowledge by teaching themselves to classify the data. Noname uses these models to create an inventory of active APIs and perform an analysis of the traffic passing through them, noting what comes in and out. Through this, the platform can identify APIs that might be passing sensitive information, like credit card and Social Security numbers.

“Noname Security doesn’t just protect the APIs, it protects the company’s data,” Gola said. “For example, an unprotected Experian API returned a credit score based simply on someone’s name and address. This is why enterprises need Noname.”

In the over $1.2 billion API management market, 70-employee, Palo Alto, California-based Noname’s competitors include Salt Security and Traceable,  among others. But Noname claims to have “hundreds” of enterprise customers either piloting its software or in full production.

“The perception in the market is that we compete with API gateways, like MuleSoft or Apigee, or web application firewalls, like Palo Alto Networks or F5 Networks. But in reality, those are our partners. We integrate with their products and enhance their API security posture,” Golan explained. “[The pandemic] helped us gain traction and sell to Fortune 500 companies as enterprise perimeters changed and API security became a top concern. Increasingly, cybersecurity is API security, yet most enterprises have no idea what APIs they’ve exposed, much less what those APIs are doing … In a post-pandemic world, modern enterprises demand broader and more flexible API security solutions.”

Previously, Noname closed a $25 million series A round in December 2020 with contributions from Lightspeed, Insight, and Cyberstarts.

Transform 2021

Elevate your enterprise data technology and strategy.

July 12-16

Presented by Planful

Artificial intelligence and machine learning (AI/ML) have already transformed industries and changed the way work gets done across the enterprise. While finance has traditionally lagged behind other departments in the AI adoption curve, that’s starting to change. Adoption of AI in finance is being spurred by digital natives (professionals who grew up in a connected world), with tech solutions finally delivering on the promise of AI/ML. Finance professionals accustomed to modern technology experiences in other areas of their lives are no longer willing to endure painstaking manual reviews and the threat of inaccurate data in their forecasts and plans.

Outside of finance, many other areas of the businesses are far beyond cutting their teeth when it comes to using AI to improve forecasting and drive decision-making. Most sales forecasts are automated, allowing sales leaders to focus on closing deals instead of crunching numbers. Further, AI has automated marketing outreach to a significant degree, enabling marketing teams to concentrate on honing their message to generate leads and build the brand.

Until now, the office of the CFO lacked native AI/ML technology built for their specific needs, with their workflow in mind. Vendors built predictive business intelligence solutions that harnessed AI to drive better decisions, but they didn’t address the core needs of the finance team. This hasn’t been lost on digital natives and immigrants working in finance — they’ve been gamers since they were six-years-old, so they know whether or not tech is designed for their role. They’re not just open to AI, now they also expect to have access to AI solutions — it’s become table stakes.

Historically, vendors haven’t done a great job of building solutions that work seamlessly and provide an exceptional user experience for finance teams — until now. Launching today, Planful “Predict: Signals” is a native AI and ML anomaly detection technology for FP&A professionals. This kind of purpose-built technology for finance can harness the power of AI to help CFOs and their teams reach their full potential. As technology continues to advance over time, the office of the CFO holds the key to unlock the power of AI/ML.

Finding signals in the noise

Cloud adoption is driving AI adoption and vice versa, and that trend accelerated drastically in 2020, when the pandemic disrupted everything from supply chains to consumer purchasing patterns and increased the urgency of finding new ways to work and innovate. Sectors that were once slow to adopt cloud and AI solutions, like health care and finance, are now either catching up or falling behind.

AI in finance frees up time for teams to execute higher-value work, like partnering with colleagues in other departments to improve forecast accuracy and drive better business outcomes. AI/ML-driven solutions make that possible.

Predict: Signals was built from the ground up to deliver a compelling and seamless user experience for finance. Finance professionals needed a product that could address the variances between predicted vs. actual numbers at the point of use, and that’s exactly what Predict: Signals delivers.

Accuracy, but at what cost?

Predict: Signals is built for finance to ensure more accurate forecasts and address finance professionals’ chief concern of data accuracy by detecting anomalies. It eliminates the need for the painstaking manual reviews that consume so much time at each monthly or quarterly close or board meeting. It works all day, every day to reduce risk and allow finance professionals to present plans with confidence and accuracy.

That’s a game changer for finance. It was one thing when sales automated forecasts with AI; sales forecasts were notoriously inaccurate anyway. The expectations are different for finance. FP&A teams pour hundreds of hours into ensuring their numbers are accurate before presenting them, and this creates trust and the expectation of accuracy from finance in a way other departments can’t match.

AI works nights, weekends, and holidays so finance pros don’t have to

The finance team’s hard-won reputation for accuracy makes letting go of the traditional manual review more difficult because they’ve sacrificed so much to establish credibility. They’ve worked nights and weekends, missed family gatherings and kids’ soccer games to check and recheck the numbers. And though time is one cost of achieving accuracy through arduous manual reviews, it’s not the only one.

Churn rates in finance departments are high (and expensive), and burnout is real at all levels, including CFOs, especially in the pressure-cooker created by the pandemic. It’s no surprise that finance has been hard hit since FP&A teams were tasked with navigating their companies through choppy waters over the past year. That increased their workload, including the need to do manual reviews to ensure accuracy.

But all the time finance spends on ensuring accuracy also means that the time-saving potential is huge. FP&A teams can drastically cut down time by embracing transformative AI-powered technology to intelligently surface anomalies hidden in massive datasets at the point of use. With Predict: Signals, FP&A teams get alerts so they can investigate variances in granular detail, plus recommendations for corrective action.

Reclaiming the finance team’s time

An intelligent anomaly detection product like Predict: Signals functions as an assistant that never sleeps, remembering everything, while learning and improving from every piece of feedback received. It won’t replace human planners, but its support will save oceans of time.

When the time required to ensure accuracy is a cost finance professionals no longer have to bear, FP&A teams can use this reclaimed time to partner with colleagues in sales to improve forecast accuracy. They can work with marketing to optimize their marketing mix, help HR build better workforce plans, and team up with operations to improve inventory and demand planning. Maybe they can even go to their kid’s soccer game.

AI and ML solutions are transforming the finance industry. CFOs who are early adopters will free their FP&A team to focus on more valuable work, giving finance leaders and digital natives alike something to get excited about, reducing churn along the way. It’s time for finance teams to reclaim their time with intelligent AI and ML solutions, reducing the cost of accuracy and multiplying efficiency exponentially. The future of finance has arrived, and it will only get brighter in the years to come.

Sanjay Vyas is Chief Technology Officer at Planful.

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