Security operations teams have a data management problem: The volume of security alerts they have to process is so high they can miss signs of an attack. In the first Voice of SecOps report from security vendor Deep Instinct, 86% of respondents said tools driven by data science — which includes artificial intelligence, machine learning, and deep learning — would make a significant impact in preventing unknown threats and reducing false positives.

Deep Instinct’s Voice of SecOps report explored strategic threats, overarching priorities, and day-to-day challenges experienced through the lens of security operations teams. According to 64% of respondents, humans are unable to keep up with the exponential cadence of cybersecurity threats. Respondents said they spend about 10 hours a week assessing false positive alerts, and 62% said threats could be missed due to the “overwhelming volume of false positives,” the report found.

IT and security teams are worried about the growing number of attacks. More than 70% of IT and security professionals said it was likely that their company will be hit by a successful ransomware attack. In the United Kingdom, 78% said they were concerned about a possible “global incident” caused by AI developed by sophisticated adversaries.

Almost two-thirds of respondents — 66% — said Solarwinds had led to the hiring of more internal IT and security professionals, with over half saying it also prompted more questions at the board and executive level about cybersecurity measures. More than 60% of companies began considering automated, AI-based solutions following the attacks on Microsoft Exchange.

Many respondents believed — 71% — that automation was the only way to address cyberattacks, and 83% said automation freed up teams to focus on high-value or more strategic attacks. If security professionals had a tool to completely eliminate false positives they would save a quarter of their time –- freeing it up to focus on the identification and prevention of upstream threats, rather than dealing with false alerts for threats that never actually existed in the first place, Deep Instinct noted in its report.

Most of the professionals believed a blend of artificial intelligence, machine learning, and deep learning was vital in the fight against cyberattacks. The key question was not whether to incorporate AI, but rather which AI technology to deploy. In Germany, 32% said a self-learning cybersecurity product would be “extremely useful.”

The Deep Instinct’s Voice of SecOps Report provides insight from 600 IT professionals, including 300 CISOs spread across multiple verticals and geographies. The research was commissioned by an independent marketing & market research company, Hayhurst Consultancy.

Read the full 2021 Voice of SecOps report from Deep Instinct.

The post Deep Instinct: AI, deep learning tools can help prevent cyberattacks appeared first on Artificial Intelligence.

Today a number of Artificial Intelligence startups are gaining momentum with significant funding and investments. These startups are not only coming up with great platforms and technologies but mastering the art of innovation. Innovation is at the core of such startups which serve the industry with something that was never explored before. Utilizing hefty amounts of investment from notable investors every now and then, they are marking the commencement of a new era of innovation with several investors coming forward to contribute to the transformative journey of emerging innovators.

Here is the list of top 10 artificial intelligence investments/funding that made the headlines in February 2020.

Worlds

Worlds, Dallas based startup, whose artificial intelligence helps large organizations observe their physical spaces to ensure security, safety, and productivity, has raised US$10 million in the first round of funding. The funding round, led by Align Capital, includes Chevron Technology Ventures, PIVA, and Hypergiant Industries. The company’s software uses multiple cameras to track spaces in 3D over time, providing a more reliable view of what’s happening than typical 2D security video would. Worlds then trains its artificial intelligence models to find unusual patterns within and across locations — and if a pattern is unusual enough, it alerts the company. Worlds says it plays in the growing extended reality (XR) market, which is expected to grow to more than US$209 billion in the next four years, where computers generate immersive experiences for physical worlds.

BenchSci

Assessing the well-being of pharmaceutical R&D by unearthing hidden patterns in procurement information is a process made less complicated by Artificial Intelligence. At least, that’s the pitch given by David Qixiang Chen, Elvis Wianda, Liran Belenzon, and Tom Leung, who cofounded BenchSci in 2015. The Toronto, Canada-based biotech firm faucets AI to run experiments that speed up drug discovery with the purpose of accelerating the velocity and high quality of the medical analysis. Last month, in an indication of confidence from new and current backers, BenchSci raised US$22 million in sequence B funding, bringing its whole raised to US$45 million.

Labelbox

Labelbox last month announced the close of a US$25 million Series B round to grow its platform that helps customers label the data needed to train artificial intelligence systems. The round was led by Andreessen Horowitz, with participation from Google’s AI-focused Gradient Ventures fund, Kleiner Perkins, and First Round Capital. The funds will be used to develop and accelerate Labelbox’s roadmap for machine learning and computer vision models by doubling the size of its engineering and sales teams. Labelbox also enables users to automate some labeling so a company can manually label all data except any that falls below a particular prediction confidence threshold, COO Brian Rieger told VentureBeat in a phone interview.

Cherre

Real estate data assortment and analytics prices can complete within the thousands and thousands of dollars. That’s why in 2016, L.D. Salmanson based Cherre, a startup that leverages AI to cost-effectively resolve property data from disparate private and non-private sources. After elevating US$9 million in October 2018, the New York-based firm introduced that it’s snagged US$16 million in enterprise and debt funding led by Intel Capital, with participation from Navitas Capital, Carthona Capital, Zigg Capital, Dreamit Ventures, and Silicon Valley Bank. “Last year was marked by incredible growth for the entire market,” stated Salmanson, who famous that this newest capital infusion brings Cherre’s complete raised to US$25 million. “This massive industry migration towards fully integrated data systems is just starting, and we’re incredibly proud to be leading the charge. We look forward to continuing working with our most demanding clients on their mission-critical data needs.”

Deep Instinct

Deep Instinct, which uses deep learning both to learn how to identify and stop known viruses and other hacking techniques, as well as to be able to identify completely new approaches that have not been identified before, has raised US$43 million in a Series C. The funding is being led by Millennium New Horizons, with Unbound (a London-based investment firm founded by Shravin Mittal), LG and Nvidia all participating. The investment brings the total raised by Deep Instinct to US$100 million, with HP and Samsung among its previous backers. The tech companies are all strategics, in that (as in the case of HP) they bundle and resell Deep Instinct’s solutions, or use them directly in their own services. The Israeli-based company is not disclosing valuation, but notably, it is already profitable.

CybelAngel

CybelAngel, a cybersecurity startup and a digital risk management platform that provides enterprises with actionable threat intelligence, announced that it has raised US$36 million in series B funding to accelerate global expansion, build strategic partnerships and advance the capabilities of its unique AI-powered technology platform. The investment round, which brings the company’s total funding to US$52 million, was led by Prime Ventures and joined by a coalition of US-based entrepreneurs and a consortium of European venture capital firms. In conjunction with the round, CybelAngel also announced that Pieter Welten, Partner at Prime Ventures, has joined its Board of Directors.

SentinelOne

SentinelOne — which provides a machine learning-based solution for monitoring and securing laptops, phones, containerized applications and the many other devices and services connected to a network — has picked up US$200 million, a Series E round of funding that it says catapults its valuation to US$1.1 billion. The funding is notable not just for its size but for its velocity: it comes just eight months after SentinelOne announced a Series D of US$120 million, which at the time valued the company around US$500 million. In other words, the company has more than doubled its valuation in less than a year — a sign of cybersecurity times. This latest round is being led by Insight Partners, with Tiger Global Management, Qualcomm Ventures LLC, Vista Public Strategies of Vista Equity Partners, Third Point Ventures and other undisclosed previous investors all participating.

OnSiteIQ

OnSiteIQ announced that it has raised a US$4.5 million series A round to grow its artificial intelligence powered safety analysis of 360-degree imagery from construction sites. In addition to supplying computer vision for risk assessment reports, the company’s platform is made to stitch together imagery so construction site managers can examine progress, skimming through footage for a guided tour like Google Street View. OnSiteIQ built the largest data set — over 100 million images — of visual construction risk information in the industry, OnSiteIQ CEO and cofounder Ardalan Khosrowpour told VentureBeat in a phone interview. The funding will be used to open new offices in Texas and California, grow the company sales team, and partner with more insurance companies to give them insights into data that may indicate risk at construction sites.

Karius

Mickey Kertes and Tim Blauwkamp — who labored collectively at Moleculo, a startup spun out of Stanford centered on commercializing know-how that generated lengthy genome sequencing reads — sought to develop a technological resolution at Karius. The Redwood City, California-based startup the 2 cofounded gives insights that allow clinicians to make remedy choices. With a blood draw, it takes a look at detecting over 1,250 potential pathogens, together with infections that may be recognized utilizing most typical testing strategies. Following a US$59 collection A funding spherical in August 2017 and a US$25 million debut financing spherical in November 2018, Karius introduced that it’s secured one other tranche — this one value US$165 million. Softbank’s Vision Fund 2 led the collection B with participation from General Catalyst, HBM, and present traders Khosla Ventures, and LightSpeed Venture Partners. bringing the startup’s whole raised to over US$200 million.

Qure.ai

Qure.ai’s AI, qXR, announced that it raised US$16 million funding to further develop its algorithm detector of malignancies and cardiac disorders. The need for a low-cost chest X-ray product becomes apparent when you think about how many tuberculosis patients in developing countries develop complications while waiting for help. As Qure.ai says, sometimes you need something that works faster than a traditional scan, especially for people living in remote locations: “Our products ensure that life-saving treatments can be delivered to patients, even in remote locations, in a fraction of the time required for traditional scan interpretation.” Here is where qXR comes in. The product works on a computer AI capable of processing X-rays and identifying up to 15 common abnormalities, including COPD, cardiac disorders, besides tuberculosis. The model was trained on X-ray scans from various medical institutions, amassing a gallery of 2.5 million images.

The post TOP 10 ARTIFICIAL INTELLIGENCE INVESTMENTS/FUNDING IN FEBRUARY 2020 appeared first on Artificial Intelligence.

Deep Instinct, the first and only cybersecurity company to successfully apply end-to-end deep learning to predict, identify, and prevent cyberattacks, announced its $43 million Series C funding. The round was led by Millennium New Horizons, with participation from Unbound, the London-based investment firm founded by Shravin Mittal, along with LG, and existing investor NVIDIA. Deep Instinct now counts four of the world’s largest technology companies amongst its investors, with HP Inc. and Samsung having participated in previous financing rounds. The investment, which brings the company’s total funding to $100 million, will be used to accelerate sales and marketing, as well as to support the expansion of business operations globally.

“Traditional cybersecurity is broken,” said Guy Caspi, co-founder and CEO of Deep Instinct. “Current solutions based on ‘assume breach’ are simply insufficient for the highly sophisticated attack landscape we all face. Deep Instinct takes an entirely new approach, preventing attacks before they are executed.”

Unlike traditional security solutions that primarily guard against known threats in the Windows operating system and help identify a cyberattack once it has already breached a system, Deep Instinct uses a patented deep learning platform trained to identify and prevent first-seen, sophisticated and advanced cyber threats. Threats are prevented anywhere within the enterprise from any type of file-based or file-less cyber attacks in zero-time, with unmatched accuracy and speed.

Deep Instinct’s deep learning protection has the lowest level of false positives of any cybersecurity provider. It is inclusive of physical and virtual networks, endpoints, and mobile, across multiple operating systems (Windows, iOS, Android, Chrome OS, and macOS).

“This significant round of new funding highlights the importance of prevention for every enterprise. The economic impact of repairing a breach is too high to ignore the need to prevent threats before they occur. The message to the market is that to fight today’s cyber threats true prevention will become more critical than detection and response.” said Lane Bess, Deep Instinct’s Chairman.

“There is no shortage of cybersecurity software providers, yet no company aside from Deep Instinct has figured out how to apply deep learning to automate malware analysis,” said Ray Cheng, Partner at Millennium New Horizons. “What excites us most about Deep Instinct is its proven ability to use its proprietary neural network to effectively detect viruses and malware no other software can catch. That genuine protection in an age of escalating threats, without the need of exorbitantly expensive or complicated systems, is a paradigm change.”

Deep Instinct recently announced an OEM partnership with HP Inc. to launch HP Sure Sense, on HP’s latest EliteBook and ZBook devices. By leveraging Deep Instinct’s deep learning threat prevention engine, HP Sure Sense provides zero-time detection and prevention against the most advanced cyber threats.

“Artificial intelligence is now sweeping across industries, bringing benefits to a wide range of vertical markets,” said Jeff Herbst, Vice President of Business Development at NVIDIA. “Deep Instinct’s unique approach in applying true deep learning to cybersecurity is yielding revolutionary breakthroughs that are being embraced by a growing market.”

The post Deep Instinct, The Deep Learning Cybersecurity Innovator, Raises $43 Million Series C Financing To Accelerate Growth appeared first on Artificial Intelligence.

The worlds of artificial intelligence and cybersecurity have become deeply entwined in recent years, as organizations work to keep up with — and ideally block — increasingly sophisticated malicious hackers. Today, a startup that’s built a deep learning solution that it claims can both identify and stop even viruses that have yet to be identified has raised a large round of funding from some big strategic partners.

Deep Instinct, which uses deep learning both to learn how to identify and stop known viruses and other hacking techniques, as well as to be able to identify completely new approaches that have not been identified before, has raised $43 million in a Series C.

The funding is being led by Millennium New Horizons, with Unbound (a London-based investment firm founded by Shravin Mittal), LG and Nvidia all participating. The investment brings the total raised by Deep Instinct  to $100 million, with HP and Samsung among its previous backers. The tech companies are all strategics, in that (as in the case of HP) they bundle and resell Deep Instinct’s solutions, or use them directly in their own services.

The Israeli-based company is not disclosing valuation, but notably, it is already profitable.

Targeting as-yet unknown viruses is becoming a more important priority as cybercrime grows. CEO and founder Guy Caspi notes that currently there are more than 350,000 new machine-generated malware created every day “with increasingly sophisticated evasion techniques, such as zero-days and APTs (Advanced Persistent Threats).” Nearly two-thirds of enterprises have been compromised in the past year by new and unknown malware attacks originating at endpoints, representing a 20% increase from the previous year, he added. And zero-day attacks are now four times more likely to compromise organizations. “Most cyber solutions on the market can’t protect against these new types of attacks and have therefore shifted to a detect-response approach,” he said, “which by design means that they ‘assume a breach’ will happen.”

While there is already a large profusion of AI-based cybersecurity tools on the market today, Caspi notes that Deep Instinct takes a critically different approach because of its use of deep neural network algorithms, which essentially are set up to mimic how a human brain thinks.

“Deep Instinct is the first and currently the only company to apply end-to-end deep learning to cybersecurity,” he said in an interview. In his view, this provides a more advanced form of threat protection than the common traditional machine learning solutions available in the market, which rely on feature extractions determined by humans, which means they are limited by the knowledge and experience of the security expert, and can only analyze a very small part of the available data (less than 2%, he says). “Therefore, traditional machine learning-based solutions and other forms of AI have low detection rates of new, unseen malware and generate high false-positive rates.” There’s been a growing body of research that supports this idea, although we’ve not seen many deep learning cybersecurity solutions emerge as a result (not yet, anyway).

He adds that deep learning is the only AI-based autonomous system that can “learn from any raw data, as it’s not limited by an expert’s technological knowledge.” In other words, it’s not based just on what a human inputs into the algorithm, but is based on huge swathes of big data, sourced from servers, mobile devices and other endpoints, that are input in and automatically read by the system.

This also means that the system can be used in turn across a number of different end points. Many machine learning-based cybersecurity solutions, he notes, are geared at Windows environments. That is somewhat logical, given that Windows and Android account for the vast majority of attacks these days, but cross-OS attacks are now on the rise.

While Deep Instinct specializes in preventing first-seen, unknown cyberattacks like APTs and zero-day attacks, Caspi notes that in the past year there has been a rise in both the amount and the impact of cyberattacks covering other areas. In 2019, Deep Instinct saw an increase in spyware and ransomware on top of an increase in the level of sophistication of the attacks that are being used, specifically with more file-less attacks using scripts and powershell, “living off the land” attacks and the use of weaponized documents like Microsoft Office files and PDFs. These sit alongside big malware attacks like Emotet, Trickbot, New ServeHelper and Legion Loader.

Today the company sells services both directly and via partners (like HP), and it’s mainly focused on enterprise users. But since there is very little in the way of technical implementation (“Our solution is mostly autonomous and all processes are automated [and] deep learning brain is handling most of the security,” Caspi said), the longer-term plan is to build a version of the product that consumers could adopt, too.

With a large part of antivirus software often proving futile in protecting users against attacks these days, that could come as a welcome addition to the market, despite how crowded it already is.

“There is no shortage of cybersecurity software providers, yet no company aside from Deep Instinct has figured out how to apply deep learning to automate malware analysis,” said Ray Cheng, partner at Millennium New Horizons, in a statement. “What excites us most about Deep Instinct is its proven ability to use its proprietary neural network to effectively detect viruses and malware no other software can catch. That genuine protection in an age of escalating threats, without the need of exorbitantly expensive or complicated systems is a paradigm change.”

The post Deep Instinct nabs $43M for a deep-learning cybersecurity solution that can suss an attack before it happens appeared first on Artificial Intelligence.