Bottom Line: Barclays’ and Kount’s co-developed new product, Barclays Transact reflects the future of how companies will innovate together to apply AI-based fraud prevention to the many payment challenges merchants face today.
Merchant payment providers have seen the severity, scope, and speed of fraud attacks increase exponentially this year. Account takeovers, card-not-present fraud, SMS spoofing, and phishing are just a few of the many techniques cybercriminals are using to defraud merchants out of millions of dollars. One in three merchants, 32%, prioritize payment providers’ fraud and security strengths over customer support and trust according to a recent YouGov survey. But it doesn’t have to be a choice between security and a frictionless transaction.
Frustrated by the limitations of existing fraud prevention systems, many payment providers are working as fast as they can to pilot AI- and machine-learning-based applications and platforms. Barclays Payment Solutions’ decision to work with AI-based solution Kount is what the future of AI-based fraud prevention for payment providers looks like.
How AI Helps Thwart Fraud And Increase Sales at Barclays
Barclays Payment Services handles 40% of all merchant payments in the UK. They’ve been protecting merchants and their customers’ data for over 50 years, and their fraud and security teams have won industry awards. For Barclays, excelling at merchant and payment security is the only option.
In order to offer an AI-based suite of tools to help merchants make their online transactions both simpler and safer, Barclays chose to partner with Kount. Their model of innovating together enables Barclays to strengthen their merchant payment business with AI-based fraud prevention and gain access to Kount’s Identity Trust Global Network, the largest network of trust and fraud-related signals. Kount gains knowledge into how they can fine-tune their AI and machine learning technologies to excel at payment services. Best of all, Barclays’ merchant customers will be able to sell more by streamlining the payment experience for their customers. The following is an overview of the Barclays Transact suite for merchants.
Barclays and Kount defined objectives for Barclay Transact: protect against increasingly sophisticated eCommerce fraud attempts, improve their merchants’ customer experiences during purchases, prepare for UK-mandated Strong Customer Authentication (SCA) by allowing businesses to take advantage of Transaction Risk Analysis (TRA) exemptions, optimize payment acceptance workflows and capitalize on Kount’s Identity Trust Global Network.
Adding urgency to the co-creation of Barclays Transact are UK regulatory requirements. To help provide clarity and support to merchants and the market from the impact of Covid-19 the Financial Conduct Authority (FCA) have agreed to delay the enforcement of a Strong Customer Authentication (SCA) until 14 September 2021 in the UK. The European Economic Area (EEA) deadline remains 31 December, 2020. Kount’s AI- and machine learning algorithms designed into Barclay Transact, tested at beta sites and fine-tuned for the first release, are effective in meeting UK government mandates.
How AI Is Turning Trust Into A Sales Accelerator At Barclays
The Barclays Payment Solutions and Kount teams believe that the more ambitious the goals for Barclays Transact to deliver value to merchants, the stronger the suite will be. Here are examples of goals businesses can achieve with this partnership:
Achieve as few false positives as possible by making real-time updates to machine learning algorithms and fine-tuning merchant responses.
Reduce the number of manual reviews for fraud analysts consistently by applying AI and machine learning to provide early warning of anomalies.
Minimize the number of chargebacks to merchant partners.
Reduce the friction and challenges merchants experience with legacy fraud prevention systems by streamlining the purchasing experience.
Enable compliance to UK-mandated regulatory requirements while streamlining merchants and their customers’ buying experiences.
Barclays Transact analyzes every transaction in real-time using Kount’s AI-based fraud analysis technology, scoring each on a spectrum of low to high risk. Each Barclays merchant’s gateway then uses this score to identify the transactions which qualify for TRA exemptions. This results in a more frictionless payment and checkout experience for customers, resulting in lower levels of shopping cart abandonment and increased sales. Higher-risk transactions requiring further inspection will still go through two-factor authentication, or be immediately declined, per the regulation and customer risk appetite. The following is an example of the workflow Barclays and Kount were able to accomplish by innovating together:
Improving buying experiences and keeping them more secure on a trusted platform is an ambitious design goal for any suite of online tools. Barclays and Kount’s successful development and launch of a co-developed product is prescient and points the way forward for payment providers who need AI expertise to battle fraud now. A bonus is how the partnership is going to enrich the Kount Identity Trust Global Network, the largest network of trust and risk signals, which is comprised of 32 billion annual interactions from more than 6,500 customers across 75+ industries. “We are excited to be partnering with Kount, because they share our goal of collaborative innovation, and a drive to deliver best-in-class shopper experiences. Thanks to Kount’s award-winning fraud detection software, the new module will not only help customers to fight fraud and prevent unwanted chargebacks, but it will also help them to maximize sales, improve customer experience, and better prepare for the introduction of SCA,” David Jeffrey, Director of Product, Barclaycard Payments said.
Bottom Line: Phishing is the leading cause of all breaches, succeeding because impersonation, redirection, and social engineering methods are always improving. And, phishing is only one way e-mails are used in fraud. Businesses need to understand if an e-mail address can be trusted before moving forward with a transaction.
Phishers’ Favorite Trojan Horse Is Office365 Followed By Cybersecurity Companies
Phishers are hiding malicious links, scripts and, in some cases, mutated software code behind legitimate Microsoft files and code to evade detection. Using legitimate code and links as a Trojan Horse to successfully launch a phishing campaign became very popular in 2019 and continues today. Cybercriminals and state-sponsored hackers have been mutating legitimate code and applications for years attempting to exfiltrate priceless data from enterprises and governments globally. Office365 is the phisher’s Trojan Horse of choice, closely followed dozens of cybersecurity companies that have seen hackers attempt to impersonate their products. Cybersecurity companies targeted include Citrix, Comodo, Imperva, Kaspersky, LastPass, Microsoft, BitDefender, CyberRoam, and others.
Using Trojan Horses To Hijack Search Results
In 2019 Microsoft discovered a sophisticated phishing attack that combined impersonation, redirection, and social engineering methods. The phishing attack relied on using links to Google search results as a Trojan Horse to deliver URLs that were poisoned so that they pointed to an attacker-controlled page, which eventually redirected to the phishing page. Microsoft discovered that a traffic generator ensured that the redirector page was the top result for specific keywords. The following graphic explains how the phishing attack was used to poison search results:
Using this workflow, phishers attempted to send phishing e-mails that relied on legitimate URLs as their Trojan Horses from legitimate domains to take advantage of the recipient’s trust. Knowing which e-mails to trust or not is becoming foundational to stopping fraud and phishing attacks.
How Kount Is Battling Sophisticated Attacks
Meanwhile, e-mail addresses can be a valuable source of information for businesses looking to prevent digital fraud. Misplaced trust can lead to chargebacks, manual reviews, and other undesirable outcomes. But, Kount’s Real-Time Identity Trust Network calculates Identity Trust Levels in milliseconds, reducing friction, blocking fraud, and delivering improved user experiences. Kount discovered that e-mail age is one of the most reliable identity trust signals there are for identifying and stopping automated fraudulent activity.
Based on their research and product development, Kount announced Email First Seen capabilities as part of its AI-powered Identity Trust Global Network. Email First Seen applies throughout the customer journey, from payments to account login to account creation. The Identity Trust Global Network consists of fraud and trust signals from over half a billion e-mail addresses. It also spans 32 billion annual interactions and 17.5 billion devices across 75 business sectors and 50-plus payment providers and card networks. The network is linked by Kount’s next-generation artificial intelligence (AI) and works to establish real-time trust for each identity behind a payment transaction, log in or account creation
E-mail Age Is Proving To Be A Reliable Indicator Of Trust
A favorite tactic of cybercriminals is to create as many new e-mail aliases as they need to deceive online businesses and defraud them of merchandise and payments. Kount is finding that when businesses can identify the age of an e-mail address, they can more accurately determine identity trust. Kount’s expertise is in fraud prevention effectiveness, relying on a combination of fraud and risk signals to generate a complete picture of authentication details. The following graphic illustrates what a Kount customer using Email First Seen will see in every e-mail they receive.
Kount’s Identity Trust Global Network relies on AI-based algorithms that can analyze all available identifiers or data points to establish real-time links between identity elements, and return identity trust decisions in real-time. Kount’s unique approach to using AI to improve customer experiences by reducing friction while blocking fraud reflects the future of fraud detection. Also, Kount’s AI can discern if additional authentication is needed to verify the identity behind the transaction and relies on half a billion e-mail addresses that are integral to AI-based analysis and risk scoring algorithms. Kount is making Email First Seen available to all existing customers for no charge. It’s been designed to be native on the Kount platform, allowing the information to be accessible in real-time to inform fraud and trust decisions.
In 2020 phishing attempts will increasingly rely on legitimate code, links, and executables as Trojan Horses to evade detection and launch phishing attacks at specific targets. Microsoft’s research and continued monitoring of phishing attempts uncovered architecturally sophisticated approaches to misdirecting victims through impersonation and social engineering.
Bottom Line: Redefining the future of fraud prevention starts by turning trust into an accelerator across every aspect of customer lifecycles, basing transactions on identity trust that leads to less friction and improved customer experiences.
Start By Turning Trust Into A Sales & Customer Experience Accelerator
AI and machine learning are proving to be very effective at finding anomalies in transactions and scoring, which are potentially the most fraudulent. Any suspicious transaction attempt leads to more work for buying customers to prove they are trustworthy. For banks, e-commerce sites, financial institutes, restaurants, retailers and many other online businesses, this regularly causes them to lose customers when a legitimate purchase is being made, and trusted customer is asked to verify their identity. Or worse, a false positive that turns away a good customer all together damages both that experience and brand reputation.
There’s a better way to solve the dilemma of deciding which transactions to accept or not. And it needs to start with finding a new way to establish identity trust so businesses can deliver better user experiences. Kount’s approach of using their Real-Time Identity Trust Network to calculate Identity Trust Levels in milliseconds reduces friction, blocks fraud, and delivers an improved user experience. Kount is capitalizing on their database that includes more than a decade of trust and fraud signals built across industries, geographies, and 32 billion annual interactions, combined with expertise in AI and machine learning to turn trust into a sales and customer experience multiplier.
How Real-Time AI Linking Leads To Real-Time Identity Trust Decisions
Design In Identity Trust So It’s The Foundation of Customer Experience
From an engineering and product design standpoint, the majority of fraud prevention providers are looking to make incremental gains in risk scoring to improve customer experiences. None, with the exception of Kount, are looking at the problem from a completely different perspective, which is how to quantify and scale identity trust. Kount’s engineering, product development, and product management teams are concentrating on how to use their AI and machine learning expertise to quantify real-time identity trust scores that drive better customer experiences across the spectrum of trust. The graphic below illustrates how Kount defines more personalized user experiences, which is indispensable in turning trust into an accelerator.
An Overview of Kount’s Technology Stack
Realize Trust Is the Most Powerful Revenue Multiplier There Is
Based on my conversations with several fraud prevention providers, they all agree that trust is the most powerful accelerator there is to reducing false positives, friction in transactions, and improving customer experiences. They all agree trust is the most powerful revenue multiplier they can deliver to their customers, helping them reduce fraud and increase sales. The challenge they all face is quantifying identity trust across the wide spectrum of transactions their customers need to fulfill every day.
Kount has taken a unique approach to identity trust that puts the customer at the center of the transactions, not just their transactions’ risk score. By capitalizing on the insights gained from their Identity Trust Global Network, Kount can use AI and machine learning algorithms to deliver personalized responses to transaction requests in milliseconds. Using both unsupervised and supervised machine learning algorithms and techniques, Kount can learn from every customer interaction, gaining new insights into how to fine-tune identity trust for every customer’s transaction.
In choosing to go in the direction of identity trust in its product strategy, Kount put user experiences at the core of their platform strategy. By combining adaptive fraud protection, personalized user experience, and advanced analytics, Kount can create a continuously learning system with the goal of fine-tuning identity trust for every transaction their customers receive. The following graphic explains their approach for bringing identity trust into the center of their platform:
Putting Customers & Their Experiences First Is Integral To Succeeding With Identity Trust
Improving customer experiences needs to be the cornerstone that drives all fraud prevention product and services road maps in 2020 and beyond. And while all fraud prevention providers are looking at how to reduce friction and improve customer experiences with fraud scoring AI-based techniques, their architectures and approaches aren’t going in the direction of identity trust. Kount’s approach is, and it’s noteworthy because it puts customer experiences at the center of their platform. How to redefine the future of fraud prevention needs to start by turning trust into a sales and customer experience accelerator, followed by designing in identity trust. Hence, it’s the foundation of all customer experiences. By combining the power of networked data and adaptive AI and machine learning, more digital businesses can turn trust into a revenue and customer experience multiplier.
The survey’s results are noteworthy because they reflect how AI and machine learning-based fraud prevention techniques are helping retailers, financial services, insurance, and restaurants to reduce false positives that, in turn, reduces friction for their customers. All industries are in an arms race with fraudsters, many of whom are using machine learning to thwart fraud prevention systems. There are a series of fraud prevention providers countering fraud and helping industries stay ahead. A leader in this field is Kount, with its Omniscore that provides digital businesses with what they need to fight fraud while providing the best possible customer experience.
The following are the key insights from the Javelin Strategy and Research report published this month:
Retailers, financial institutions, restaurants, and insurance companies need to invest in fraud mitigation at the same rate as new product innovation, with retail and banking leading the way. Restaurants and insurance are lagging in their adoption of fraud mitigation techniques and, as a result, tend to experience more fraud. The insurance industry has a friendly fraud problem that is hard to catch. Over half of the financial institutions interviewed, 52% plan to invest in additional technologies to secure existing accounts, and 46% plan to invest in better identity-verification measures. Based on the survey, banks appear to be early adopters of AI and machine learning for fraud prevention. The study makes an excellent point that banking via virtual assistants is still nascent and constrained by the lack of information sharing within the ecosystem, which restricts authentication measures to PINs and passwords.
57% of all businesses are adding new products and services as their leading digital innovation strategy in 2020, followed by refining the user experience (55%) and expanding their digital strategy teams. Comparing priorities for digital innovation across the four industries reflects how each is approaching their omnichannel strategy. The banking industry places the highest priority on improving the security of existing user accounts at 52% of financial institutions surveyed. Improving security is the highest priority in banking today, according to the survey results shown below. This further validates how advanced banking and financial institutions are in their use of AI and machine learning for fraud prevention.
Digital businesses plan to improve their omnichannel strategies by improving their website, mobile app, and online catalog customer experiences across all channels in addition to better integration between digital and physical services is how. 40% of respondents are actively investing in improving the integration between digital and physical services. That’s an essential step for ensuring a consistently excellent user experience across websites, product catalogs, buy online and pick up in-store, and consistent user experiences across all digital and physical channels.
69% of all digital businesses interviewed are planning to make additional fraud investments this year. Banking and financial institutions dominate the four industries surveyed in the plans for additional fraud investment. 82% of consumer banks are planning to invest in additional fraud detection technologies. Insurers are least likely to invest in fraud detection technologies in 2020. The study notes that this can be attributed to insurers’ unique challenges with first-party fraud or fraud committed by legitimate policyholders, which is poorly addressed by many mainstream fraud controls.
Using AI-based scoring techniques to detect stolen credit card data being used online or in mobile apps, dominates financial institutions’ priorities today. 34% of financial institutions cite their top fraud threat being the use of stolen credit card data used online or in mobile apps. 18% say account takeovers are their most important area to reduce fraud. Financial institutions lead all others in fraud technology investments to thwart fraud, with managing digital fraud risk being the highest priority of all compared to the three other industries represented in the survey.
52% of all financial institutions say that improving the security of existing user accounts leads all digital investment priorities in 2020. What’s significant about this finding is that it outpaces adding new digital products and services and improving identity verification of new users. This is another factor that contributes to financial institutions’ leadership role in relying on AI and machine learning to improve fraud detection and deterrence.
80% of fraud specialists using AI-based platforms believe the technology helps reduce payments fraud.
63.6% of financial institutions that use AI believe it is capable of preventing fraud before it happens, making it the most commonly cited tool for this purpose.
Fraud specialists unanimously agree that AI-based fraud prevention is very effective at reducing chargebacks.
The majority of fraud specialists (80%) have seen AI-based platforms reduce false positives, payments fraud, and prevent fraud attempts.
AI is proving to be very effective in battling fraud based on results achieved by financial institutions as reported by senior executives in a recent survey, AI Innovation Playbook published by PYMNTS in collaboration with Brighterion. The study is based on interviews with 200 financial executives from commercial banks, community banks, and credit unions across the United States. For additional details on the methodology, please see page 25 of the study. One of the more noteworthy findings is that financial institutions with over $100B in assets are the most likely to have adopted AI, as the study has found 72.7% of firms in this asset category are currently using AI for payment fraud detection.
Taken together, the findings from the survey reflect how AI thwarts payments fraud and deserves to be a high priority in any digital business today. Companies, including Kount and others, are making strides in providing AI-based platforms, further reducing the risk of the most advanced, complex forms of payments fraud.
Why AI Is Perfect For Fighting Payments Fraud
Of the advanced technologies available for reducing false positives, reducing and preventing fraud attempts, and reducing manual reviews of potential payment fraud events, AI is ideally suited to provide the scale and speed needed to take on these challenges. More specifically, AI’s ability to interpret trend-based insights from supervised machine learning, coupled with entirely new knowledge gained from unsupervised machine learning algorithms are reducing the incidence of payments fraud. By combining both machine learning approaches, AI can discern if a given transaction or series of financial activities are fraudulent or not, alerting fraud analysts immediately if they are and taking action through predefined workflows. The following are the main reasons why AI is perfect for fighting payments fraud:
Payments fraud-based attacks are growing in complexity and often have a completely different digital footprint or pattern, sequence, and structure, which make them undetectable using rules-based logic and predictive models alone. For years e-commerce sites, financial institutions, retailers, and every other type of online business relied on rules-based payment fraud prevention systems. In the earlier years of e-commerce, rules and simple predictive models could identify most types of fraud. Not so today, as payment fraud schemes have become more nuanced and sophisticated, which is why AI is needed to confront these challenges.
AI brings scale and speed to the fight against payments fraud, providing digital businesses with an immediate advantage in battling the many risks and forms of fraud. What’s fascinating about the AI companies offering payments fraud solutions is how they’re trying to out-innovate each other when it comes to real-time analysis of transaction data. Real-time transactions require real-time security. Fraud solutions providers are doubling down on this area of R&D today, delivering impressive results. The fastest I’ve seen is a 250-millisecond response rate for calculating risk scores using AI on the Kount platform, basing queries on a decades-worth of data in their universal data network. By combining supervised and unsupervised machine learning algorithms, Kount is delivering fraud scores that are twice as predictive as previous methods and faster than competitors.
AI’s many predictive analytics and machine learning techniques are ideal for finding anomalies in large-scale data sets in seconds. The more data a machine learning model has to train on, the more accurate its predictive value. The greater the breadth and depth of data, a given machine learning algorithm learns from means more than how advanced or complex a given algorithm is. That’s especially true when it comes to payments fraud detection where machine learning algorithms learn what legitimate versus fraudulent transactions look like from a contextual intelligence perspective. By analyzing historical account data from a universal data network, supervised machine learning algorithms can gain a greater level of accuracy and predictability. Kount’s universal data network is among the largest, including billions of transactions over 12 years, 6,500 customers, 180+ countries and territories, and multiple payment networks. The data network includes different transaction complexities, verticals, and geographies, so machine learning models can be properly trained to predict risk accurately. That analytical richness includes data on physical real-world and digital identities creating an integrated picture of customer behavior.
Bottom Line: Payments fraud is insidious, difficult to stop, and can inflict financial harm on any business in minutes. Battling payment fraud needs to start with a pre-emptive strategy to thwart fraud attempts by training machine learning models to quickly spot and act on threats then building out the strategy across every selling and service channel a digital business relies on.
Today’s Threatscape Has Made “Trust But Verify” Obsolete
The threatscape every business operates in today is proving the old model of “trust but verify” obsolete and in need of a complete overhaul. To compete and grow in the increasingly complex and lethal threatscape of today, businesses need more adaptive, contextually intelligent security solutions based on the Zero Trust Security framework. Zero Trust takes a “never trust, always verify, enforce least privilege” approach to privileged access, from inside or outside the network. John Kindervag was the first to see how urgent the need was for enterprises to change their approach to cybersecurity, so he created the Zero Trust Security framework in 2010 while at Forrester. Chase Cunningham, Principal Analyst at Forrester, is a mentor to many worldwide wanting to expand their knowledge of Zero Trust and frequently speaks and writes on the topic. If you are interested in cybersecurity in general and Zero Trust specifically, be sure to follow his blog.
The top ten cybersecurity companies reflect the speed and scale of innovation happening today that are driving the highest levels of investment this industry has ever seen. The following are the top ten cybersecurity companies to watch in 2019:
Absolute(ABT.TO) – One of the world’s leading commercial enterprise security solutions, serving as the industry benchmark for endpoint resilience, visibility, and control. The company enables more than 12,000 customers with self-healing endpoint security, always-connected visibility into their devices, data, users, and applications whether endpoints are on or off the network, and the ultimate level of control and confidence required for the modern enterprise. Embedded in over one billion endpoint devices, Absolute delivers intelligence and real-time remediation capabilities that equip enterprises to stop data breaches at the source.
Absolute’s research found that 42% of all endpoints are unprotected at any given time, and 100% of endpoint security tools eventually fail. As a result, IT leaders see a negative ROI on their security spend. What makes Absolute one of the top 10 security companies to watch in 2019 is their purpose-driven design to mitigate this universal law of security decay.
Enterprises rely on Absolute to cut through the complexity to identify failures, model control options, and refocus security intent. Rather than perpetuating organizations’ false sense of security, Absolute enables uncompromised endpoint persistence, builds resilience and delivers the intelligence needed to ensure security agents, applications, and controls continue functioning and deliver value as intended. Absolute has proven very effective in validating safeguards, fortifying endpoints, and stopping data security compliance failures. The following is an example of the Absolute platform at work:
BlackBerry Artifical Intelligence and Predictive Security – BlackBerry is noteworthy for how quickly they are reinventing themselves into an enterprise-ready cybersecurity company independent of the Cylance acquisition. Paying $1.4B in cash for Cylance brings much-needed AI and machine learning expertise to their platform portfolio, an acquisition that BlackBerry is moving quickly to integrate into their product and service strategies. BlackBerry Cylance uses AI and machine learning to protect the entire attack surface of an enterprise with automated threat prevention, detection, and response capabilities. Cylance is also the first company to apply artificial intelligence, algorithmic science, and machine learning to cyber security and improve the way companies, governments, and end users proactively solve the world’s most challenging security problems. Using a breakthrough mathematical process, BlackBerry Cylance quickly and accurately identifies what is safe and what is a threat, not just what is in a blacklist or whitelist. By coupling sophisticated math and machine learning with a unique understanding of a hacker’s mentality, BlackBerry Cylance provides the technology and services to be truly predictive and preventive against advanced threats. The following screen from CylancePROTECT provides an executive summary of CylancePROTECT usage, from the number of zones and devices to the percentage of devices covered by Auto-Quarantine and Memory Protection, Threat Events, Memory Violations, Agent Versions, and Offline Days for devices.
Centrify – Centrify is redefining the legacy approach to Privileged Access Management by delivering cloud-ready Zero Trust Privilege to secure modern enterprise attack surfaces. Centrify Zero Trust Privilege helps customers grant least privilege access based on verifying who is requesting access, the context of the request, and the risk of the access environment. Industry research firm Gartner predicted Privileged Access Management (PAM) to be the second-fastest growing segment for information security and risk management spending worldwide in 2019 in their recent Forecast Analysis: Information Security and Risk Management, Worldwide, 3Q18 Update (client access required). By implementing least privilege access, Centrify minimizes the attack surface, improves audit and compliance visibility, and reduces risk, complexity, and costs for the modern, hybrid enterprise. Over half of the Fortune 100, the world’s largest financial institutions, intelligence agencies, and critical infrastructure companies, all trust Centrify to stop the leading cause of breaches – privileged credential abuse. PAM was also named a Top 10 security project for 2019 in Gartner’s Top 10 Security Projects for 2019 (client access required). CloudFlare – Cloudflare is a web performance and security company that provides online services to protect and accelerate websites online. Its online platforms include Cloudflare CDN that distributes content around the world to speed up websites, Cloudflare Optimizer that enables web pages with ad servers and third-party widgets to download Snappy software on mobiles and computers, CloudFlare Security that protects websites from a range of online threats including spam, SQL injection, and DDOS, Cloudflare Analytics that gives insight into website’s traffic including threats and search engine crawlers, Keyless SSL that allows organizations to keep secure sockets layer (SSL) keys private, and Cloudflare applications that help its users install web applications on their websites.
CrowdStrike – Applying machine learning to endpoint detection of IT network threats is how CrowdStrike is differentiating itself in the rapidly growing cybersecurity market today. It’s also one of the top 25 machine learning startups to watch in 2019. Crowdstrike is credited with uncovering Russian hackers inside the servers of the US Democratic National Committee. The company’s IPO was last Tuesday night, with an initial $34/per share price. Their IPO generated $610M at a valuation at one point reaching nearly $7B. Their Falcon platform stops breaches by detecting all attacks types, even malware-free intrusions, providing five-second visibility across all current and past endpoint activity while reducing cost and complexity for customers. CrowdStrike’s Threat Graph provides real-time analysis of data from endpoint events across the global crowdsourcing community, allowing detection and prevention of attacks based on patented behavioral pattern recognition technology.
Hunters.AI – Hunters.AI excels at autonomous threat hunting by capitalizing on its autonomous system that connects to multiple channels within an organization and detects the signs of potential cyber-attacks. They are one of the top 25 machine learning startups to watch in 2019. What makes this startup one of the top ten cybersecurity companies to watch in 2019 is their innovative approach to creating AI- and machine learning-based algorithms that continually learn from an enterprise’s existing security data. Hunters.AI generates and delivers visualized attack stories allowing organizations to more quickly and effectively identify, understand, and respond to attacks. Early customers, including Snowflake Computing, whose VP of Security recently said, “Hunters.AI identified the attack in minutes. In my 20 years in security, I have not seen anything as effective, fast, and with high fidelity as what Hunters can do.” The following is a graphic overview of how their system works:
Idaptive – Idaptive is noteworthy for the Zero Trust approach they are taking to protecting organizations across every threat surface they rely on operate their businesses dally. Idaptive secures access to applications and endpoints by verifying every user, validating their devices, and intelligently limiting their access. Their product and services strategy reflects a “never trust, always verify, enforce least privilege” approach to privileged access, from inside or outside the network. The Idaptive Next-Gen Access platform combines single single-on (SSO), adaptive multifactor authentication (MFA), enterprise mobility management (EMM) and user behavior analytics (UBA). They have over 2,000 organizations using their platform today. Idaptive was spun out from Centrify on January 1st of this year.
Kount – Kount has successfully differentiated itself in an increasingly crowded cybersecurity marketplace by providing fraud management, identity verification and online authentication technologies that enable digital businesses, online merchants and payment service providers to identify and thwart a wide spectrum of threats in real-time. Kount has been able to show through customer references that their customers can approve more orders, uncover new revenue streams, and dramatically improve their bottom line all while minimizing fraud management cost and losses. Through Kount’s global network and proprietary technologies in AI and machine learning, combined with policy and rules management, their customers thwart online criminals and bad actors driving them away from their site, their marketplace and off their network. Kount’s continuously adaptive platform learns of new threats and continuously updates risk scores to further thwart breach and fraud attempts. Kount’s advances in both proprietary techniques and patented technology include: Superior mobile fraud detection, Advanced artificial intelligence, Multi-layer device fingerprinting, IP proxy detection and geo-location, Transaction and custom scoring, Global order linking, Business intelligence reporting, Comprehensive order management, Professional and managed services. Kount protects over 6,500 brands today.
MobileIron – The acknowledged leader in Mobile Device Management software, MobileIron’s latest series of developments make them noteworthy and one of the top ten cybersecurity companies to watch in 2019. MobileIron was the first to deliver key innovations such as multi-OS mobile device management (MDM), mobile application management (MAM), and BYOD privacy controls. Last month MobileIron introduced zero sign-on (ZSO), built on the company’s unified endpoint management (UEM) platform and powered by the MobileIron Access solution. “By making mobile devices your identity, we create a world free from the constant pains of password recovery and the threat of data breaches due to easily compromised credentials,” wrote Simon Biddiscombe, MobileIron’s President and Chief Executive Officer in his recent blog post, Single sign-on is still one sign-on too many. Simon’s latest post, MobileIron: We’re making history by making passwords history, provides the company’s vision going forward with ZSO. Zero sign-on eliminates passwords as the primary method for user authentication, unlike single sign-on, which still requires at least one username and password. MobileIron paved the way for a zero sign-on enterprise with its Access product in 2017, which enabled zero sign-on to cloud services on managed devices. Enterprise security teams no longer have to trade off security for better user experience, thanks to the MobileIron Zero Sign-On.
Sumo Logic – Sumo Logic is a fascinating cybersecurity company to track because it shows the ability to take on large-scale enterprise security challenges and turn them into a competitive advantage. An example of this is how quickly the company achieved FedRAMP Ready Designation, getting listed in the FedRAMP Marketplace. Sumo Logic is a secure, cloud-native, machine data analytics service, delivering real-time, continuous intelligence from structured, semi-structured, and unstructured data across the entire application lifecycle and stack. More than 2,000 customers around the globe rely on Sumo Logic for the analytics and insights to build, run, and secure their modern applications and cloud infrastructures. With Sumo Logic, customers gain a multi-tenant, service-model advantage to accelerate their shift to continuous innovation, increasing competitive advantage, business value, and growth. Founded in 2010, Sumo Logic is a privately held company based in Redwood City, Calif. and is backed by Accel Partners, Battery Ventures, DFJ, Franklin Templeton, Greylock Partners, IVP, Sapphire Ventures, Sequoia Capital, Sutter Hill Ventures and Tiger Global Management.