Apr
10
2019
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The right way to do AI in security

Artificial intelligence applied to information security can engender images of a benevolent Skynet, sagely analyzing more data than imaginable and making decisions at lightspeed, saving organizations from devastating attacks. In such a world, humans are barely needed to run security programs, their jobs largely automated out of existence, relegating them to a role as the button-pusher on particularly critical changes proposed by the otherwise omnipotent AI.

Such a vision is still in the realm of science fiction. AI in information security is more like an eager, callow puppy attempting to learn new tricks – minus the disappointment written on their faces when they consistently fail. No one’s job is in danger of being replaced by security AI; if anything, a larger staff is required to ensure security AI stays firmly leashed.

Arguably, AI’s highest use case currently is to add futuristic sheen to traditional security tools, rebranding timeworn approaches as trailblazing sorcery that will revolutionize enterprise cybersecurity as we know it. The current hype cycle for AI appears to be the roaring, ferocious crest at the end of a decade that began with bubbly excitement around the promise of “big data” in information security.

But what lies beneath the marketing gloss and quixotic lust for an AI revolution in security? How did AL ascend to supplant the lustrous zest around machine learning (“ML”) that dominated headlines in recent years? Where is there true potential to enrich information security strategy for the better – and where is it simply an entrancing distraction from more useful goals? And, naturally, how will attackers plot to circumvent security AI to continue their nefarious schemes?

How did AI grow out of this stony rubbish?

The year AI debuted as the “It Girl” in information security was 2017. The year prior, MIT completed their study showing “human-in-the-loop” AI out-performed AI and humans individually in attack detection. Likewise, DARPA conducted the Cyber Grand Challenge, a battle testing AI systems’ offensive and defensive capabilities. Until this point, security AI was imprisoned in the contrived halls of academia and government. Yet, the history of two vendors exhibits how enthusiasm surrounding security AI was driven more by growth marketing than user needs.

Apr
09
2019
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Accenture announces intent to buy French cloud consulting firm

As Google Cloud Next opened today in San Francisco, Accenture announced its intent to acquire Cirruseo, a French cloud consulting firm that specializes in Google Cloud intelligence services. The companies did not share the terms of the deal.

Accenture says that Cirruseo’s strength and deep experience in Google’s cloud-based artificial intelligence solutions should help as Accenture expands its own AI practice. Google TensorFlow and other intelligence solutions are a popular approach to AI and machine learning, and the purchase should help give Accenture a leg up in this area, especially in the French market.

“The addition of Cirruseo would be a significant step forward in our growth strategy in France, bringing a strong team of Google Cloud specialists to Accenture,” Olivier Girard, Accenture’s geographic unit managing director for France and Benelux said in a statement.

With the acquisition, should it pass French regulatory muster, the company would add a team of 100 specialists trained in Google Cloud and G Suite to the an existing team of 2,600 Google specialists worldwide.

The company sees this as a way to enhance its artificial intelligence and machine learning expertise in general, while giving it a much stronger market placement in France in particular and the EU in general.

As the company stated, there are some hurdles before the deal becomes official. “The acquisition requires prior consultation with the relevant works councils and would be subject to customary closing conditions,” Accenture indicated in a statement. Should all that come to pass, then Cirruseo will become part of Accenture.

Apr
03
2019
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Okta unveils $50M in-house venture capital fund

Identity management software provider Okta, which went public two years ago in what was one of the first pure-cloud subscription-based company IPOs, wants to fund the next generation of identity, security and privacy startups.

At its big customer conference Oktane, where the company has also announced a new level of identity protection at the server level, chief operating officer Frederic Kerrest (pictured above, right, with chief executive officer Todd McKinnon) will unveil a $50 million investment fund meant to back early-stage startups leveraging artificial intelligence, machine learning and blockchain technology.

“We view this as a natural extension of what we are doing today,” Okta senior vice president Monty Gray told TechCrunch. Gray was hired last year to oversee corporate development, i.e. beef up Okta’s M&A strategy.

Gray and Kerrest tell TechCrunch that Okta Ventures will invest capital in existing Okta partners, as well as other companies in the burgeoning identity management ecosystem. The team managing the fund will look to Okta’s former backers, Sequoia, Andreessen Horowitz and Greylock, for support in the deal sourcing process.

Okta Ventures will write checks sized between $250,000 and $2 million to eight to 10 early-stage businesses per year.

“It’s just a way of making sure we are aligning all our work and support with the right companies who have the right vision and values because there’s a lot of noise around identity, ML and AI,” Kerrest said. “It’s about formalizing the support strategy we’ve had for years and making sure people are clear of the fact we are helping these organizations build because it’s helpful to our customers.”

Okta Ventures’ first bet is Trusted Key, a blockchain-based digital identity platform that previously raised $3 million from Founders Co-Op. Okta’s investment in the startup, founded by former Microsoft, Oracle and Symantec executives, represents its expanding interest in the blockchain.

“Blockchain as a backdrop for identity is cutting edge if not bleeding edge,” Gray said.

Okta, founded in 2009, had raised precisely $231 million from Sequoia, Andreessen Horowitz, Greylock, Khosla Ventures, Floodgate and others prior to its exit. The company’s stock has fared well since its IPO, debuting at $17 per share in 2017 and climbing to more than $85 apiece with a market cap of $9.6 billion as of Tuesday closing.

Apr
02
2019
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Pixeom raises $15M for its software-defined edge computing platform

Pixeom, a startup that offers a software-defined edge computing platform to enterprises, today announced that it has raised a $15 million funding round from Intel Capital, National Grid Partners and previous investor Samsung Catalyst Fund. The company plans to use the new funding to expand its go-to-market capacity and invest in product development.

If the Pixeom name sounds familiar, that may be because you remember it as a Raspberry Pi-based personal cloud platform. Indeed, that’s the service the company first launched back in 2014. It quickly pivoted to an enterprise model, though. As Pixeom CEO Sam Nagar told me, that pivot came about after a conversation the company had with Samsung about adopting its product for that company’s needs. In addition, it was also hard to find venture funding. The original Pixeom device allowed users to set up their own personal cloud storage and other applications at home. While there is surely a market for these devices, especially among privacy-conscious tech enthusiasts, it’s not massive, especially as users became more comfortable with storing their data in the cloud. “One of the major drivers [for the pivot] was that it was actually very difficult to get VC funding in an industry where the market trends were all skewing towards the cloud,” Nagar told me.

At the time of its launch, Pixeom also based its technology on OpenStack, the massive open-source project that helps enterprises manage their own data centers, which isn’t exactly known as a service that can easily be run on a single machine, let alone a low-powered one. Today, Pixeom uses containers to ship and manage its software on the edge.

What sets Pixeom apart from other edge computing platforms is that it can run on commodity hardware. There’s no need to buy a specific hardware configuration to run the software, unlike Microsoft’s Azure Stack or similar services. That makes it significantly more affordable to get started and allows potential customers to reuse some of their existing hardware investments.

Pixeom brands this capability as “software-defined edge computing” and there is clearly a market for this kind of service. While the company hasn’t made a lot of waves in the press, more than a dozen Fortune 500 companies now use its services. With that, the company now has revenues in the double-digit millions and its software manages more than a million devices worldwide.

As is so often the case in the enterprise software world, these clients don’t want to be named, but Nagar tells me they include one of the world’s largest fast food chains, for example, which uses the Pixeom platform in its stores.

On the software side, Pixeom is relatively cloud agnostic. One nifty feature of the platform is that it is API-compatible with Google Cloud Platform, AWS and Azure and offers an extensive subset of those platforms’ core storage and compute services, including a set of machine learning tools. Pixeom’s implementation may be different, but for an app, the edge endpoint on a Pixeom machine reacts the same way as its equivalent endpoint on AWS, for example.

Until now, Pixeom mostly financed its expansion — and the salary of its more than 90 employees — from its revenue. It only took a small funding round when it first launched the original device (together with a Kickstarter campaign). Technically, this new funding round is part of this, so depending on how you want to look at this, we’re either talking about a very large seed round or a Series A round.

Mar
28
2019
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Kong raises $43M Series C for its API platform

Kong, the open core API management and life cycle management company previously known as Mashape, today announced that it has raised a $43 million Series C round led by Index Ventures. Previous investors Andreessen Horowitz and Charles River Ventures (CRV), as well as new investors GGV Capital and World Innovation Lab, also participated. With this round, Kong has now raised a total of $71 million.

The company’s CEO and co-founder Augusto Marietti tells me the company plans to use the funds to build out its service control platform. He likened this service to the “nervous system for an organization’s software architecture.”

Right now, Kong is just offering the first pieces of this, though. One area the company plans to especially focus on is security, in addition to its existing management tools, where Kong plans to add more machine learning capabilities over time, too. “It’s obviously a 10-year journey, but those two things — immunity with security and machine learning with [Kong] Brain — are really a 10-year journey of building an intelligent platform that can manage all the traffic in and out of an organization,” he said.

In addition, the company also plans to invest heavily in its expansion in both Europe and the Asia Pacific market. This also explains the addition of World Innovation Lab as an investor. The firm, after all, focuses heavily on connecting companies in the U.S. with partners in Asia — and especially Japan. As Marietti told me, the company is seeing a lot of demand in Japan and China right now, so it makes sense to capitalize on this, especially as the Chinese market is about to become more easily accessible for foreign companies.

Kong notes that it doubled its headcount in 2018 and now has more than 100 enterprise customers, including Yahoo! Japan, Ferrari, SoulCycle and WeWork.

It’s worth noting that while this is officially a Series C investment, Marietti is thinking of it more like a Series B round, given that the company went through a major pivot when it moved from being Mashape to its focus on Kong, which was already its most popular open-source tool.

“Modern software is now built in the cloud, with applications consuming other applications, service to service,” said Martin Casado, general partner at Andreessen Horowitz . “We’re at the tipping point of enterprise adoption of microservices architectures, and companies are turning to new open-source-based developer tools and platforms to fuel their next wave of innovation. Kong is uniquely suited to help enterprises as they make this shift by supporting an organization’s entire service architecture, from centralized or decentralized, monolith or microservices.”

Mar
27
2019
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Microsoft, Adobe and SAP prepare to expand their Open Data Initiative

At last year’s Microsoft Ignite conference, the CEOs of Microsoft, Adobe and SAP took the stage to announce the launch of the Open Data Initiative. The idea behind this effort was to make it easier for their customers to move data between each others’ services by standardizing on a common data format and helping them move their data out of their respective silos and into a single customer-chosen data lake. At this week’s Adobe Summit, the three companies today announced how they plan to expand this program as they look to bring in additional partners.

“The intent of the companies joining forces was really to solve a common customer problem that we hear time and time again, which is that there are high-value business data tends to be very siloed in a variety of different applications,” Alysa Taylor, Microsoft’s corporate vice president, Business Applications & Global Industry, told me. “Being able to extract that data, reason over that data, garner intelligence from that data, is very cost-prohibitive and it’s very manual and time-consuming.”

The core principle of the alliance is that the customers own their data and they should be able to get as much value out of it as they can. Ideally, having this common data schema means that the customer doesn’t have to figure out ways to transform the data from these vendors and can simply flow all of it into a single data lake that then in turn feeds the various analytics services, machine learning systems and other tools that these companies offer.

At the Adobe Summit today, the three companies showed their first customer use case based on how Unilever is making use of this common data standard. More importantly, though, they also stressed that the Open Data Initiative is indeed open to others. As a first step, the three companies today announced the formation of a partner advisory council.

“What this basically means is that we’ve extended it out to key participants in the ecosystem to come and join us as part of this ODI effort,” Adobe’s VP of Ecosystem Development Amit Ahuja told me. “What we’re starting with is really a focus around two big groups of partners. Number one is, who are the other really interesting ISVs who have a lot of this core data that we want to make sure we can bring into this kind of single unified view. And the second piece is who are the major players out there that are trying to help these customers around their enterprise architecture.”

The first 12 partners that are joining this new council include Accenture, Amadeus, Capgemini, Change Healthcare, Cognizant, EY, Finastra, Genesys, Hootsuite, Inmobi, Sprinklr and WPP . This is very much a first step, though. Over time, the group expects to expand far beyond this first set of partners and include a much larger group of stakeholders.

“We really want to make this really broad in a way that we can quickly make progress and demonstrate that what we’re talking about from a conceptual process has really hard customer benefits attached to it,” Abhay Kumar, SAP’s global vice president, Global Business Development & Ecosystem, noted. The use cases the alliance has identified focus on market intelligence, sales intelligence and services intelligence, he added.

Today, as enterprises often pull in data from dozens of disparate systems, making sense of all that information is hard enough, but to even get to this point, enterprises first have to transform it and make it usable. To do so, they then have to deploy another set of applications that massages the data. “I don’t want to go and buy another 15 or 20 applications to make that work,” Ahuja said. “I want to realize the investment and the ROI of the applications that I’ve already bought.”

All three stressed that this is very much a collaborative effort that spans the engineering, sales and product marketing groups.

Mar
25
2019
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Scalyr launches PowerQueries for advanced log management

Log management service Scalyr today announced the beta launch of PowerQueries, its new tools for letting its users create advanced search operations as they manage their log files and troubleshoot potential issues. The new service allows users to perform complex actions to group, transform, filter and sort their large data sets, as well as to create table lookups and joins. The company promises these queries will happen just as fast as Scalyr’s standard queries and that getting started with these more advanced queries is pretty straightforward.

Scalyr founder and chairman Steve Newman argues that the company’s competitors may offer similar tools, but that “their query languages are too complex, hard-to-learn and hard-to-use.” He also stressed that Scalyr made a conscious decision not to use any machine learning tools to power this and its other services to help admins and developers prioritize issues and instead decided to focus on its query language and making it easier for its users to manage their logs that way.

“So we thought about how we could leverage our strengths — real-time performance, ease-of-use and scalability — to provide similar but better functionality,” he said in today’s announcement. “As a result, we came up with a set of simple but powerful queries that address advanced use cases while improving the user experience dramatically. Like the rest of our solution, our PowerQueries are fast, easy-to-learn and easy-to-use.”

Current Scalyr customers cover a wide range of verticals. They include the likes of NBCUniversal, Barracuda Networks, Spiceworks, John Hopkins University, Giphy, OkCupid and Flexport. Currently, Scalyr has more than 300 paying customers. As Newman stressed, more than 3,500 employees from these customers regularly use the service. He attributes this to the fact that it’s relatively easy to use, thanks to Scalyr’s focus on usability.

The company raised its last funding round — a $20 million Series A round — back in 2017. As Scalyr’s newly minted CEO Christine Heckart told me, though, the company is currently seeing rapid growth and has quickly added headcount in recent months to capitalize on this opportunity. Given this, I wouldn’t be surprised if we saw Scalyr raise another round in the not-so-distant future, especially considering that the log management market itself is also rapidly growing (and has changed quite a bit since Scalyr launched back in 2011), as more companies start their own digital transformation projects, which often allows them to replace some of their legacy IT tools with more modern systems.

Mar
20
2019
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Skedulo raises $28M for its mobile workforce management service

Skedulo, a service that helps businesses manage their mobile employees, today announced that it has raised a $28 million Series B funding round led by M12, Microsoft’s venture fund. Existing investors Blackbird and Castanoa Ventures also participated in this round.

The company’s service offers businesses all the necessary tools to manage their mobile employees, including their schedules. A lot of small businesses still use basic spreadsheets and email to do this, but that’s obviously not the most efficient way to match the right employee to the right job, for example.

“Workforce management has traditionally been focused on employees that are sitting at a desk for the majority of their day,” Skedulo CEO and co-founder Matt Fairhurst told me. “The overwhelming majority — 80 percent — of workers will be deskless by 2020 and so far, there has been no one that has addressed the needs of this growing population at scale. We’re excited to help enterprises confront these challenges head-on so they can compete and lean into rapidly changing customer and employee expectations.”

At the core of Skedulo, which offers both a mobile app and web-based interface, is the company’s so-called “Mastermind” engine that helps businesses automatically match the right employee to a job based on the priorities the company has specified. The company plans to use the new funding to enhance this tool through new machine learning capabilities. Skedulo will also soon offer new analytics tools and integrations with third-party services like HR and financial management tools, as well as payroll systems.

The company also plans to use the new funding to double its headcount, which includes hiring at least 60 new employees in its Australian offices in Brisbane and Sydney.

As part of this round, Priya Saiprasad, principal of M12, will join Skedulo’s board of directors. “We found a strong sense of aligned purpose with Priya Saiprasad and the team at M12 — and their desire to invest in companies that help reduce cycles in a person’s working day,” Fairhurst said. “Fundamentally, Skedulo is a productivity company. We help companies, the back-office and mobile workforce, reduce the number of cycles it takes to get work done. This gives them time back to focus on the work that matters most.”

Mar
19
2019
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AI has become table stakes in sales, customer service and marketing software

Artificial intelligence and machine learning has become essential if you are selling sales, customer service and marketing software, especially in large enterprises. The biggest vendors from Adobe to Salesforce to Microsoft to Oracle are jockeying for position to bring automation and intelligence to these areas.

Just today, Oracle announced several new AI features in its sales tools suite and Salesforce did the same in its customer service cloud. Both companies are building on artificial intelligence underpinnings that have been in place for several years.

All of these companies want to help their customers achieve their business goals by using increasing levels of automation and intelligence. Paul Greenberg, managing principal at The 56 Group, who has written multiple books about the CRM industry, including CRM at the Speed of Light, says that while AI has been around for many years, it’s just now reaching a level of maturity to be of value for more businesses.

“The investments in the constant improvement of AI by companies like Oracle, Microsoft and Salesforce are substantial enough to both indicate that AI has become part of what they have to offer — not an optional [feature] — and that the demand is high for AI from companies that are large and complex to help them deal with varying needs at scale, as well as smaller companies who are using it to solve customer service issues or minimize service query responses with chatbots,” Greenberg explained.

This would suggest that injecting intelligence in applications can help even the playing field for companies of all sizes, allowing the smaller ones to behave like they were much larger, and for the larger ones to do more than they could before, all thanks to AI.

The machine learning side of the equation allows these algorithms to see patterns that would be hard for humans to pick out of the mountains of data being generated by companies of all sizes today. In fact, Greenberg says that AI has improved enough in recent years that it has gone from predictive to prescriptive, meaning it can suggest the prospect to call that is most likely to result in a sale, or the best combination of offers to construct a successful marketing campaign.

Brent Leary, principle at CRM Insights, says that AI, especially when voice is involved, can make software tools easier to use and increase engagement. “If sales professionals are able to use natural language to interact with CRM, as opposed to typing and clicking, that’s a huge barrier to adoption that begins to crumble. And making it easier and more efficient to use these apps should mean more data enters the system, which result in quicker, more relevant AI-driven insights,” he said.

All of this shows that AI has become an essential part of these software tools, which is why all of the major players in this space have built AI into their platforms. In an interview last year at the Adobe Summit, Adobe CTO Abhay Parasnis had this to say about AI: “AI will be the single most transformational force in technology,” he told TechCrunch. He appears to be right. It has certainly been transformative in sales, customer service and marketing.

Mar
19
2019
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Salesforce update brings AI and Quip to customer service chat experience

When Salesforce introduced Einstein, its artificial intelligence platform in 2016, it was laying the ground work for artificial intelligence underpinnings across the platform. Since then the company has introduced a variety of AI enhancements to the Salesforce product family. Today, customer service got some AI updates.

The goal of any customer service interaction is to get the customer answers as quickly as possible. Many users opt to use chat over phone, and Salesforce has added some AI features to help customer service agents get answers more quickly in the chat interface. (The company hinted that phone customer service enhancements are coming.)

For starters, Salesforce is using machine learning to deliver article recommendations, response recommendations and next best actions to the agent in real time as they interact with customers.  “With Einstein article recommendations, we can use machine learning on past cases and we can look at how articles were used to successfully solve similar cases in the past, and serve up the best article right in the console to help the agent with the case,” Martha Walchuk, senior director of product marketing for Salesforce Service Cloud explained.

Salesforce Service Console. Screenshot: Salesforce

The company is also using similar technology to provide response recommendations, which the agent can copy and paste into the chat to speed up the time to response. Before the interaction ends, the company can offer the next best action (which was announced last year) based on the conversation. For example, they could offer related information, an upsell recommendation or whatever type of action the customer defines.

Salesforce is also using machine learning to help route each person to the most appropriate customer service rep. As Salesforce describes it, this feature uses machine learning to filter cases and route them to the right queue or agent automatically, based on defined criteria such as best qualified agent or past outcomes.

Finally, the company is embedding Quip, the company it acquired in 2016 for $750 million, into the customer service console to allow agents to communicate with one another to find answers to difficult problems. That not only helps solve the issues faster, the conversations themselves become part of the knowledge base, which Salesforce can draw upon to help teach the machine learning algorithms about the correct responses to commonly asked questions in the future.

As with the Oracle AI announcement this morning, this use of artificial intelligence in sales, service and marketing is part of a much broader industry trend, as these companies try to inject intelligence into workflows to make them run more efficiently.

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