AI Stats News: Humans Plus AI 20X More Effective In Cybersecurity Defense Than Traditional Methods

Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI highlighted the role of augmented intelligence, combining human intelligence with artificial intelligence to produce better results in cybersecurity defense and in getting more business value from the use of IoT data.

AI business impact

Combining cybersecurity talent and AI-enabled technology results in 20x more effective attack surface coverage than traditional methods; using AI accelerates by 73% the time to evaluate the breach-worthiness of a vulnerability; by augmenting humans with AI, companies are able to find and close critical vulnerabilities 40% faster, reducing the vulnerability risk window; organizations that have utilized an augmented approach to security testing for two or more years are up to 200% stronger against cyber attacks than they were in their first year [Synack report based on data from hundreds of thousands of hours of security tests]

The most significant predictor in realizing value from Internet of Things (IoT) initiatives across an organization is the heavy use of artificial intelligence (AI); 90% of survey respondents heavily using AI in their IoT operations reported exceeding value expectations; 79% of senior leaders are involved in IoT project decisions, and 92% of those leaders say that AIoT value exceeds expectations; companies using IoT data to speed up operations without AI saw a 32% increase; companies adding AI to the mix saw speeds improve by 53% [SAS, Deloitte, Intel and IDC survey of 450 business leaders worldwide]

AI business adoption

23% of law firm and in-house counsel are using artificial intelligence or machine learning technology in their practices; among those using AI or machine learning, document review is the most common use (47%), followed by eDiscovery (41%) and legal research (31%) [Bloomberg Law’s 2019 Legal Operations & Technology survey of 500 in-house and law firm practitioners]

42% of respondents report they are in the beginning phases of looking at how AI could benefit procurement; only 6% report they are actively using AI in their procurement digitization initiatives right now, with another 21% testing AI utilization—the greatest share of these are almost exclusively in large and multinational organizations [Dun & Bradstreet]

Two thirds of UK financial institutions report they use machine learning (ML) in some form; ML is most commonly used in anti-money laundering (AML) and fraud detection as well as in customer-facing applications (e.g. customer services and marketing); some firms also use ML in areas such as credit risk management, trade pricing and execution, as well as general insurance pricing and underwriting; the biggest reported constraints are internal to firms, such as legacy IT systems and data limitations [The Bank of England (BoE) and Financial Conduct Authority (FCA) survey of 287 financial institutions]

AI government adoption

Analysis of about 150 Federal departments and agencies identified 171 different uses of machine learning. Two of the leading agencies were the Securities and Exchange Commission and the Social Security Administration. The SEC uses machine learning to help identify scammers who may engage in insider trading. The SSA uses machine learning to catch possible errors in draft decisions that spell out who receives payouts on claims [Fortune and Stanford Policy Lab]

The Life of Data, the fuel for AI

21% of IT workers don’t actually know what a ‘cyber-attack’ constitutes; 43% admitted to being unaware of how to defend their company from a cyber-attack, with 32% relying on external agencies for crisis support; only 12% know what their company’s business continuity plan fully constitutes [ survey of 1,032 IT workers]

87% of IT and data protection professionals say cloud computing has enabled better and/or more cost-efficient protection of their organization’s data [ESG]

56% of consumers say they would wait longer than one month to shop again on a retailer’s website that compromised their information; 66% are concerned about their personal data being stolen as a result of shopping online; 48% do not believe small online retailers properly store their data online [SiteLock]

AI predictions

By 2024, with proactive, hyper-speed operational changes and market reactions, artificial intelligence (AI)-powered enterprises will respond to customers, competitors, regulators, and partners 50% faster than their peers [IDC]

Funding in AI has grown YoY every year for a decade. While we predict another new peak in 2020, that will be the crescendo. YoY growth rates have slowed from 67% in 2017 to 25% in 2018. Unless there’s an unforeseen spike or outlier round in Q4 2019, the growth rate will slow again. With more than 2,600 companies globally, the AI startup ecosystem is a saturated market. Over half of those companies and about two-thirds of all funding events are attributed to machine learning and deep learning (which are two out of 13 AI subcategories in our taxonomy). While 69% of funding came from early rounds this year, there are far fewer new entrants recorded, so fundraising should skew to later stages in 2020. The biggest signal of a slowdown is that 20 AI companies have raised unicorn-sized funding rounds in the past 12 months. This cannot be sustainable. Why such high valuations? Because of the industry-specific transformation potential, as we see in transportation, finance, and healthcare [Forrester]

AI market forecasts

The worldwide collaborative robots (cobots) market will reach $9.7 billion by the end of 2025 [Tractica]

AI quotable quotes

“Beside China, at least 74 other countries are also engaging in AI-powered surveillance, including half of advanced liberal democracies” [Reuters and The National Security Commission on Artificial Intelligence]

“China is ahead in two areas. One is in the face recognition surveillance area. And another one is in financial technology. This does not mean that they’re ahead (in) AI overall”—Eric Schmidt

“My core argument is that no one does AI evaluation well, because national AI capability is such a fuzzy concept… When comparing different countries’ AI abilities, it’s probably more useful to clearly specify what you’re trying to compare”—Jeff Ding

“People tend to underestimate the importance of culture, but even policymakers are affected by it. If you grow up thinking AI is going to go well for humanity, you tend to be more optimistic about it – it’s that simple”—Dganit Gal

“This is what I believe the next few years will be about [for Amazon Alexa]: reasoning and making it more personal, with more context. It’s like bringing everything together to make these massive decisions”—Rohit Prasad

“AI is a tool or an expression of the humans that build it”—Carissa Schoenick

“We have sold 19 books on Amazon so far. Being a writer is a tough job, even if you are an artificial intelligence”—Andreas Refsgaard, co-creator of, an online bookstore where neural networks write the books, create the cover art, price the merchandise, and even write the reviews (H/T The Batch)


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