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Traceable raises $20 million for AI system that shields cloud app APIs from cyberattacks

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Traceable, a startup developing an end-to-end cloud app security solution, today emerged from stealth with $20 million in venture equity financing. Newly flush with capital, CEO Jyoti Bansal intends to focus on acquiring customers globally while growing Traceable's team and accelerating R&D. Cloud-native apps are often built with hundreds or even thousands of API microservices (i.e., loosely coupled services), making them difficult to protect at scale. Gartner predicts that by 2022, API abuses will be the most frequent attack vector, which isn't surprising considering API calls represented 83% of web traffic as of 2018. Traceable ostensibly protects these APIs with machine learning algorithms that analyze app activity from the user and the session all the way down to the code.


The top cybersecurity news sources you should be reading

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Staying on top of cybersecurity trends and threats is almost a full-time job. The industry expands every day as new applications come online for the internet of things, machine learning and artificial intelligence. But cybercriminals are innovating, too. Not a day passes without news of a major ransomware attack or phishing scheme. Businesses large and small now must learn the intricacies of data breaches and the dark web, negotiating with hackers, buying cyberinsurance and training employees on the best cyberhygiene.


Adoption Picking Up for AI in Cybersecurity; More Skilled Humans Needed Too

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AI is increasingly being put to use in the technology stacks of cybersecurity companies, but not at the expense of human experts who guide the rollout and work alongside the smart tools. Before 2019, one in five cybersecurity software and service providers were employing AI, according to a study last year by Capgemini Research Institute, in a review of recent research published in DarkReading. Adoption was found to be "poised to skyrocket" by the end of 2020, with 63% of the firms planning to deploy AI in their solutions. Planned use in IT operations and the Internet of Things are predicted to see the most uptick. Increased adoption of AI does not mean that security professionals on IT staffs are ready to hand off their responsibilities.


Why are Artificial Intelligence systems biased?

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A machine-learned AI system used to assess recidivism risks in Broward County, Fla., often gave higher risk scores to African Americans than to whites, even when the latter had criminal records. The popular sentence-completion facility in Google Mail was caught assuming that an "investor" must be a male. A celebrated natural language generator called GPT, with an uncanny ability to write polished-looking essays for any prompt, produced seemingly racist and sexist completions when given prompts about minorities. Amazon found, to its consternation, that an automated AI-based hiring system it built didn't seem to like female candidates. Commercial gender-recognition systems put out by industrial heavy-weights, including Amazon, IBM and Microsoft, have been shown to suffer from high misrecognition rates for people of color.


ExtraHop Named to the Forbes AI 50 List for 2020

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ExtraHop, the leader in cloud-native network detection and response, announced that it has been named to the 2020 Forbes AI 50 list for its advances and innovation in the use of machine learning and artificial intelligence for cybersecurity. Forbes evaluated hundreds of applications to recognize 50 private, U.S.-based companies for their innovative use of artificial intelligence to drive outcomes for customers and within their own organizations. ExtraHop was selected on the basis of company growth and revenue, as well as for its leadership in the use of machine learning and AI for cybersecurity threat detection, investigation, and response. "Increasingly advanced cybersecurity attacks, including those sponsored by nation-states, require a sophisticated approach to threat detection and response," said Arif Kareem, CEO of ExtraHop. "This is not possible without machine learning and AI. At ExtraHop, we've applied these techniques to enable enterprises to detect and respond to cyber threats across complex enterprise and cloud environments at scale. Our inclusion on this list recognizes the strength of our approach."


AI and security: Machine learning is a threat detection game-changer

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Organizations are deluged with billions of security events every day, far too many for human analysts to cope with. But security analysts have a powerful ally in their corner: machine learning is tipping the advantage toward defenders. Machine learning (ML) is changing the approach of organizations to threat detection and how they adapt and adopt cybersecurity processes. The idea is not just to identify and prevent threats, but to mitigate them as well. ML has the power to comprehend threats in real time, to understand the infrastructure of a company and its network design and attack vectors, and to protect and defend it with human talent and machine power. The algorithm--the machine--is capable of the unthinkable when it comes to data mining, data crunching, and data correlation, since it does what it does best tirelessly, without complaint, and without having a bad day.


How business leaders can use AI to bridge the cybersecurity skills gap

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Cyberattacks on the likes of several tech giants have brought to the fore the challenge of bridging the skills gap in the cybersecurity space in India. And, artificial intelligence being the latest buzzword of the tech industry, is being touted as one of the key solutions to the cybersecurity skills gap. According to a report, it is estimated that there will be 3.5 million unfilled cybersecurity jobs globally by the year 2021. And therefore, companies are struggling to find adequate qualified people to assist in creating an intelligent cybersecurity framework. The challenge has become apparent in the last five to ten years with a sharp increase in cyberattacks, all the way from ransomware to zero-day malware to now sneaky crypto-mining attacks.


How AI Is Preventing Data Breaches In 3 Major Industries

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Artificial intelligence (AI) and machine learning are allowing both businesses and consumers to boost their cybersecurity to unprecedented levels. In a recent post, we examined six ways that AI is leading the way towards rock-solid information security. In case you missed it, read it here. For this article, we'll take a closer look at how AI and machine learning are letting three major industries safeguard their data better. In each of these sectors, websites not only contain a wealth of sensitive information but also have a high volume of visitors every day.


Does the Human Touch + AI = The Future of Work?

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Artificial intelligence has long caused fear of job loss across many sectors as companies look for ways to cut costs, support workers and become more profitable. But new research suggests that even in STEM-based sectors like cybersecurity, AI simply can't replace some traits found only in humans, such as creativity, intuition and experience. There's no doubt, AI certainly has its place. And most business leaders agree that AI is important to the future success of their company. A recent survey found CEOs believe the benefits of AI include creating better efficiencies (62 percent), helping businesses remain competitive (62 percent), and allowing organizations to gain a better understanding of their customers, according to Ernst and Young.


Deep learning-based attack detection AI engine

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TOKYO, June 30, 2020 /PRNewswire-PRWeb/ -- About Cyneural While cyber-attack defenses generally respond by detecting specific patterns of "signatures" that indicate malicious access, complex or unknown attacks that utilize AI or BOTs can be difficult to detect or can result in false positives. This is why cyber-attack defenses also need to take advantage of technology with flexibility such as AI. Against this backdrop, Cyber Security Cloud developed its own attack detection AI engine, Cyneural, in August 2019. "Cyneural" uses a feature extraction engine that utilizes the knowledge cultivated through CSC's research on web access and various attack methods. It builds multiple types of training models to help detect not only common attacks but also unknown cyber-attacks and false positives at a higher speed. About Cyneural being used in Shadankun and WafCharm Since the development of Cyneural, CSC has been operating it by utilizing the large amount of data that they have.