Advances in AI are widely understood to have implications for cybersecurity. Articles have emphasized the effect of AI on the cyber offense-defense balance, and commentators can be found arguing either that cyber will privilege attackers or defenders. For defenders, arguments are often made that AI will enable solutions like formal verification of all software--and for some well-equipped companies, this may be true. This conversation, however, does not match the reality for most companies. "Trailing-edge organizations," as we term them, rely heavily on legacy software, poorly staff security roles, and struggle to implement best practices like rapid deployment of security patches. These decisions may be the result of corporate inertia, but may also be the result of a seemingly-rational calculation that attackers may not bother targeting a firm due to lack of economic incentives, and as a result, underinvestment in defense will not be punished. This approach to security may have been sufficient prior to the development of AI systems, but it is unlikely to remain viable in the near future. We argue that continuing improvements in AI's capabilities poses additional risks on two fronts: First, increased usage of AI will alter the economics of the marginal cyberattack and expose these trailing-edge organizations to more attackers, more frequently. Second, AI's advances will enable attackers to develop exploits and launch attacks earlier than they can today--meaning that it is insufficient for these companies to attain parity with today's leading defenders, but must instead aim for faster remediation timelines and more resilient software. The situation today portends a dramatically increased number of attacks in the near future. Moving forward, we offer a range of solutions for both organizations and governments to improve the defensive posture of firms which lag behind their peers today.

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DataVisor, a company that delivers the most sophisticated Artificial Intelligence (AI) powered fraud management solutions, announced a partnership with Equifax, making the global data, analytics and technology company its primary consumer identity data partner. DataVisor will enhance and extend its market-leading behavior analytics and fraud detection solution by combining rich consumer identity data from Equifax with powerful AI signals, for zero-lag detection and a frictionless customer experience. "Fraud losses are expected to grow to more than $600 billion by 2023. For businesses to protect both themselves and their customers from the well-orchestrated tactics of fraudsters, a layered approach to fraud detection is critical" DataVisor will integrate Digital Identity Trust, Secure Multi Factor Authentication (MFA) and Account Verification solutions from Equifax into its next-generation fraud detection platform, enhancing its fraud models for improved detection accuracy and enabling data enrichment features for end-clients globally. When layered together, these Equifax solutions can provide a 90% reduction in fraud risk and a 30% reduction in operational overhead, according to analysis conducted by Equifax.

For years, people have held abstract fears about AI – from scores of lost jobs to out of control robots. But as AI has evolved and more companies experiment with the technology, the fears have also evolved – as risks become more apparent. A new report from Harvard Business Review Analytic Services, "An Executive's Guide to Real-World AI," offers insights and first-hand accounts from top chief information and digital officers who are leading the charge in their organizations, ranging from Raytheon to Capital One. These fears are part of the reason that many early AI and automation use cases are limited to the task level, with final decisions and actions performed by humans, notes the report. But leaders in AI are addressing the concerns head on in their work.

Hundreds of data scientists work in the Equifax Data and Analytics Lab. They focus on connecting the companies' unique data with customers' unique needs. The data scientists do this through a relentless pursuit of innovation in machine learning, predictive analytics, and explainable AI. AI stands for Artificial Intelligence, a software technology that makes machines think like humans. It also makes them behave like humans. When we are involved in any activity, we learn as we go along.

Equifax Inc. EFX yesterday unveiled Advanced Model Engine, an AI-enabled modular model development and deployment tool. This latest tool utilizes big data and distributed computing for faster construction of models that can be easily deployed with applications across industries and use cases. It enhances model performance using machine learning techniques, alternative and trended data, use case configuration, and processing power to test model configurations. The engine comprises Equifax NeuroDecision Technology -- a machine learning technology for regulatory-compliant, and advanced neural network modeling in credit scoring. It is currently available through Equifax Ignite -- the company's premier data and advanced analytics portfolio.

Summary: GDPR carries many new data and privacy requirements including a "right to explanation". On the surface this appears to be similar to US rules for regulated industries. We examine why this is actually a penalty and not a benefit for the individual and offer some insight into the actual wording of the GDPR regulation which also offers some relief. GDPR is now just about 60 days away and there's plenty to pay attention to especially in getting and maintaining permission to use a subscriber's data. If you're just starting out in the EU there are some new third party offerings that promise to keep track of things for you (Integris, Kogni, and Waterline all emphasized this feature at the Strata Data San Jose conference this month).

In January Google's parent company, Alphabet, announced the launch of Chronicle – an artificial intelligence-based solution for the cybersecurity industry – promising "the power to fight cyber crime on a global scale." There are mixed opinions on the value and readiness of artificial intelligence (AI) in our industry. Just last year Google's own Heather Adkins, director of information security and privacy addressed the crowd at TechCrunch Disrupt 2017 and criticized the over use of artificial intelligence for the cybersecurity industry. Adkins argued that the implementation of artificial intelligence relies too heavily on feedback, "to learn what is good and bad…but we're not sure what good and bad is." She went on to say that companies should invest in more human talent and less technology.

According to IBM Security's new "Future of Identity Study," users of both mobile and desktop are wide awake to what's happening with each new high profile breach, and it's made them change their priorities. Strong security and privacy are now at the forefront of the average user's concerns, especially when it comes to banking, financial, online marketplaces, and their email accounts. That's a sharp contrast to research from Gartner in 2008, which also surveyed 4,000 people and found that most consumers didn't want to change the way they did passwords or security, and were highly resistant to using password managers or adopting additional security measures around log-ins. "Despite widespread security concerns," Gartner concluded ten years ago, "consumers continue to rely on service providers to protect their safety and persist in using unsafe password management practices, preferring to maintain the status quo rather than exploring new security methods." IBM's 2018 report surveyed nearly 4,000 adults from around the world. Most respondents were in the US.

Technological advances in artificial intelligence are fuelling a new race between hackers and those toiling to protect cybersecurity networks. Cybersecurity is always a race between offence and defence but new tools are giving companies that employ them a leg up on those trying to steal their data. Whereas past responses to cybercrimes often looked for known hacking methods long after they occurred, AI techniques using machine learning scan huge volumes of data to detect patterns of abnormal behaviour that are imperceptible to humans. Experts expect machines will become so sophisticated that they'll develop answers to questions that humans won't clearly understand. David Decary-Hetu, assistant professor of criminology at the University of Montreal, says defenders have an edge right now in using artificial intelligence.