Monthly Archives: July 2016

Let’s Know Uber, Airbnb & Etsy Spellbound Their First 1000 Customers

Recently, I read one blog on the web in which it was discussed how Uber, Airbnb, and Etsy attracted their first 1000 customers. I was quite with the explanation, so though to share the information that I read.

As we all know that there are lots of new businesses, who struggle often to find their first customers. The major challenge is with startups in their sharing economy, which launches as platforms connecting independent service providers with consumers.

However, you can take Uber. It is a platform with two-sided, linking people, who are looking for rides with people, who have rides to offer. Well, it is an exactly same idea as Airbnb that also connects people needing rooms with home-owners. These are the companies, who need to search for users on both supply and demand sides to launch as a platform service.

There is one saying “Poaching Customers Is Something All Competitors Do In Different Ways”.

A Harvard Business School’s Thales Teixeira, Lumry Family Associate Professor of Business Administration says, “When you have a two-sided platform, you have to acquire both the customers and the services”.

“It’s the classic chicken-and-egg problem,” he says. You can’t have one without the other, but which one do you find first—the customer chicken or the service egg? “As a small company, you cannot afford to focus on both with the same amount of effort. You may need to prioritize one side.”

In order to teach a new course on eCommerce marketing next spring, Teixeira made it his goal to find an answer. He researched and studied three most popular and successful startups, including Uber, Etsy, and Airbnb so that he can find some commonalities in how these businesses resolved the quandary.

In a new HBS case, Teixeira described that Uber, Airbnb, and Etsy: Gaining the first Thousand Customers. These three platforms are focusing on getting the service side of the equation first, customers second. But there’s a catch. “It’s not just the chicken and the egg, you also want to select the right eggs,” explains Teixeira. “If you acquire the wrong eggs and ostriches come out, then you are in trouble. The chickens will run for the hills.”

Contemplate Like Customers

It’s already clear to the founders of the apartment-sharing website from the commencement that Airbnb need to search people, who are eager to list their homes before searching people interesting in staying in them.

“If you don’t have a supply of houses and apartments, people are not going to come,” says Teixeira. The problem was, where to find people willing to let strangers stay in their places. It’s not like they could go around San Francisco knocking on doors.

Even the founders named Brian Chesky and Joe Gebbia thought like customers themselves so that they can figure it out where they would go if Airbnb didn’t exist. However, it didn’t take them long to find out the answer: Craigslist.

The entrepreneurs predicted that they could do a better job of making apartments attractive compare to the online classified website; however, they had to siphon away its customers. In order to that Gebbia and Chesky have developed software to hack Craigslist to citation the contact information of property owners; therefore, sent them a pitch to list on Airbnb also.

This strategy worked very well. Property owners gathered their chances of searching a potential renter with nothing to lose, and Airbnb had ready to supply homes with which it could entice customers.

“Poaching customers is something all competitors do in different ways,” says Teixeira. “If you are a website and you are providing content to users publicly, others can grab that information.” It’s not enough to just take someone else’s customers, though, he warns—you’ve got to give them something better than they had before.

Create a Better Experience

The Airbnb founders comprehend that they have a problem once they had apartment owners on the hook. When it comes to talking about the subpar photos, which property owners were taking for Craigslist on their iPhones would never work for customers looking for an alternative to a hotel.

“The first time a person goes on Airbnb, they are comparing the quality of photos to hotels that take glamorized shots,” says Teixeira. “They needed to compete at that level.”

Chesky and Gebbia did something very amazing in order to do that would never be scalable. Hiring experienced and professional photographers in order to go to property owners’ homes so that they can take pictures. However, the gambit also worked extremely hard in order to make the website more attractive than the usual.

“The underlying principle of this is you should help your suppliers portray themselves in the best way possible, even if that is not scalable,” concludes Teixeira. “If you don’t have customers, there is nothing to scale.”

Uber, a ride-sharing application, followed the same strategy. Instead of starting out with Uber Pool or Uber X in which drivers use their own cars as the company has started with black cars that can be driven by professional drivers.

This way they can make sure that their customers would have an excellent experience effectively every single time they used the service. Then they could rely on customers as well so that they can spread the news of that experience by word of mouth. Regarding this Teixeira says, “That’s why you get the supply side first—if you get the right suppliers, the customers will experience their high-quality service and then do the marketing for you.”

In order to search the right eggs with which to introduce its business, Etsy has followed a right certainly non-scalable strategy: scrubbing craft fairs across the country to know the best vendors at each, and pitching them on opening up an online store on the website. “They first brought their customers, and then they brought other artisans who followed the customers.” Once Etsy had the first-tier artisans on the site, the next tier naturally followed them.

Sequencing Is Everything

When it comes to talking about Airbnb and Uber, they were also keen to know that how they choose to enlarge, picking the right cities at the right time to boost the success. The startup has researched that which cities had the biggest discrepancy between supply and demand for taxis since the main competition of Uber was taxi cab companies.

They then launched during times when that demand was likely to be the highest, for example during the holidays when people tend to stay out late partying. It also ran promotions during large concerts or sporting events, when big crowds of people all needed cabs at the same time, and an individual might be more likely to take a chance on an unfamiliar company named Uber.

Therefore, the company also learned about customers in one swoop. Teixeira says, “First, they figured out how to get a bunch of customers all in one night when the demand was high. Then, they made sure this first group of users had a great experience and brought in the next wave of customers via word-of-mouth.”

On this fact, the company also layered that once users comprehended how easy it was as it was only matter of time before they begin using it to go to work and after that shopping for groceries.

However, Airbnb also followed a similar strategy with its rollout, introducing in Denver in 2008 to accord with the lack of hotels pace while the Democratic National Convention and adding new cities at times when they had major conventions or other events.

Along with the real demand, the strategy has another benefit – Teixeira says, “Your competitors don’t see you as a threat since you are not taking away from their demand.” It is already late for them to do anything about it by the time you have the position in the market.

Introducing in different situations of increasing demand and low supply helping startups obtaining the accurate type of customers. All those who are early adopters, who might be more merciful of a company while it works out the bends. Teixeira says, “You are still a startup.” He also adds, “you have to find people who are willing to accept your flaws and cut you some slack. Satisfying all their needs and wants is just not feasible at this early stage.”

Now, what? From 1000 to 100,000,000

A company can also commence thinking about how to enlarge their customers base through means of marketing. Teixeira wrote a sequel case study to solve the problem, Airbnb, Etsy, Uber: Growing from one thousand to one million customers, and it is currently working on a third entry in the series, which will observe how a platform can go from one million to many millions of customers.

In every single case, you will find different strategies. However, word-of-mouth will work for the first thousand and it is not going to help you to reach to a million. “You have to be more proactive and control the acquisition process, which word-of-mouth does not allow for.”

This is the situation where digital marketing will help out, enabling different companies to get targeted customers through different search ads or social media at the affordable rate.

Teixeira says, “It’s highly targetable and you can do it on the cheap” – adding that digital marketing also makes it a lot easy for varied companies to quickly iterate its advertising message, altering it in order to figure it out what works best. “Only after passing the millionth customer can you go into advertising on traditional media. That’s when you need massive scale, so you go to mass marketing.”

It must expect the main purpose of advertising to attain the best effects in boosting new customers as the company grows. Teixeira says, “Some tools are better for the beginning, some are better when you are bigger.”

He also adds, “It’s not about, should I use digital marketing or word-of-mouth or TV ads. The question only makes sense when you say, ‘I am at this stage, what approach should I take?’ Only when you answer that question will you know what tool is most appropriate.” He also added, “You need the right size of eggs for each stage of your nest.”

How Big Data Effects Its Developer?

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With the constant internet social grass wars, the land grab is gradually becoming much better. Now, the internet is more blocked and controlled than earlier as some of the companies have started controlling 95% of the social data.

However, this term big data has been thrown long ago over the past 15 months. Here, I mentioning is user data that mainly from social businesses and can be leveraged to develop other applications and businesses if done within the confines of a company API.

Let’s consider some of the best examples: Facebook’s developer, product architect, entrepreneur, etc. may want to study the names, pictures or shares. Well, what about Snapchat: shares or number of sent items. Tesla: car location, energy consumption. Instagram: users, hearts, and comments. A complete list goes on and on and never ends.

I was thinking that what makes a good app; therefore, I researched and spent some of the time on it. Well, the answer is extremely simple: data. It is precise: users and their respective meta-data. So, users are data. Without data, it is not going to work the way you want it to.

However, the follow-up always been like this “How Do I Get Users?”

Already obtainable social websites want you to trust that by connecting to them and through them, users will surely come. But only that were true. The thing is that to break the record, you can’t buy users. You also cannot connect to obtainable websites to influence users or anything between. However, most of the users are got bored of new and somewhat the same software. For developing a successful application, distribution is the most difficult part.

A lot of developers prefer to go deep into the social graph on Facebook. They were also injecting evocative data in a classy way to all sorts of web and app products. Moreover, developed also used to request big and complex datasets without getting regulated by bandwidth limits. It’s not that some of the big companies allow you to use their data doesn’t make their data accurate and useful.

From the last couple of years, there is a huge shift in it. Unfortunately, one of the best ways to show such change is to look at the rise and fall of Zynga. With Facebook opened their API and allowed users to go deep into the Facebook graph, more than any other company took benefit and built a wonderful gaming business through Facebook Graph API.

Facebook started making changes to how developers can interact with particular data and as instantly as Zynga grew, they chop. A lot of other companies are also there, small and big, that have also affected and suffered same decrease.

These days, the modern yet top social big data players trap people like you and me in like a kid in a candy store with no coin to spend, there will be only infinite temptation and promise of sweet solutions. With the announcement of high-quality and fast data exchange or deep penetration into graphs is the text of all the modern big businesses’ API documentation. Well, it is true that you can have the data at your desired speed, but when you hit the API verge, the feed will go from Niagara Falls to a leaking sink spout. But what if your business relies on its capability to instantly retrieve data?

Like this, you are capable of accessing set graphs and do deep analysis, but make sure that you dig a litter deeper so that you get and they provide you with 1-3 percent of anything meaningful about a specific location, person, hearts, shares and so on.

Ultimately, Zynga and other lesser-known businesses showed that if you develop a business on top of another business, you are at the urge of their decisions. Gradually, they will change the way you interact with their data that has huge inferences on the permanence and effectiveness of your business. The promised land of green meadows, boundless and fast data requests and deep diffusion of user data is finished.

The real meadows are a tease as the internet and social businesses have matured in the time, and the value is the walled garden. The capability of market and sell advertising wins every time by keeping user data within the limits of the core business. The markets are extremely small to support that model.

Still, there is huge potential, so we all just need to think in a more changed manner. However, you can’t only think like this that “I’m going to come up with an idea and leverage an existing community to make it thrive.” Because APIs are not allowing these types of development anymore. As Uber has done recently, Pinterest and Snapchat, the mindset of the entrepreneur wants to be one of the new community, specialized approaches.

Therefore, you make sure that you don’t make the social be the simple pillar of the business, so consider it like a feature. Depending purely on social without all-embracing premise is paradoxically private, and it is the quickest way of the back of the app store.

What Sales Mistakes Do Enterprise Software Companies Make?

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Do you know that selling into the enterprise is hard for early-to-mid-stage B2B software and SaaS companies? It is much difficult for enterprise customers to pay for their solution on a lasting basis without seeing their sales growth stall out at $15-25 million ARR.

There are lots of companies like Workday, Salesforce, NetSuite, Athena health and much more that have already found long-lasting B2B sales success and crooked their companies into pillars of the enterprise SaaS ecosystem.

But the wide range of enterprise companies is facing this challenge. There are various factors that can slow a company’s B2B sales progress, comprising timing issues, competitive challenges, product deficiencies and more. According to me, a lot of private enterprise software companies are there that make big sales mistakes:

Are You Fully Deprived?

We all have competitors and they come in many different shapes and sizes. Recently, Mikkel Savne, a Zendesk CEO, says, “There’s an incredible variety of software products out there.” It is essential to read the “About Us” page of all the companies in your targeted market, and you can also study that how many of those view your space as theirs.

When it comes to talking about the competitors, there are various other private companies and big enterprises with huge sales organizations. You can also compete with customers own inside developed efforts that may be inferior, but can be tough to overcome given ties to their own solution.

Other competition also comprises services and reselling companies, which representing third-party vendors. Recently, I read one news of AppDirect co-CEO Daniel Sakes, who points out that more than 70% of on-premise software sales have usually been channel-based.

While speaking he also adds, “80% of on-premise software vendors operate a channel program to enable other companies to sell their products, while only 20% of SaaS vendors operate similar programs.”

This station and reselling obstacle have highlighted a competitive challenge for SaaS companies that are fronting off against traditional software vendors. Providing more information about the game, it is a zero-sum game among all the vendors; therefore, signing a customer means a loss for someone else.

A single step that frequently overlooked is simply engaging with customers about the competitive land, comprising potential and won/lost targets. It is essential to invest time in collecting important and observant information about competitors and adjust consequently.

Upright Product-market Fit is Not Acceptable

The President/COO of Alteryx ‘George Mathew’ says, “In today’s enterprise software market, it’s important to define a user experience that is 100 times better than the status quo.”

For this, enormous reasons are there like the fact that apathy, incumbency, and government are all working against. It means looking for a way to be exponentially better with fewer resources. Consequently, the focus is the key and ultimate thing.

David Sacks, a Yammer co-founder, spoke about this when he took over as CEO of Zenefits earlier this year. “Companies execute better when they ruthlessly prioritize and sequence their efforts,” Sacks wrote. “For us, that means hyper-focusing on the small business market where we have product-market fit.”

But does anyone know about hyper-focusing? Where have we product-market fit? It means chasing all those market sections for which your product has a sole and convincing solution. Various private B2B companies have already developed solutions, which are working for a subset of customers; however, there are still challenged with sales cycles longer than ideal.

Many a time, the market-fit problem with developing enterprise companies stems from inadequate focus. Well, cruel ordering as advocated by Sacks doesn’t come logically. Market fit can enhance from having the most ongoing dialogue with customers.

For targeted sectors, your solution only be just a nice-to-have service and not compelling enough to overcome distinctive enterprise barriers. With more attention, companies can get a sweet spot by developing an acute understanding of customers’ requirements in a particular segment that is more nuanced than the broader sector needs. Struggle the need to broaden your focus too much in order to succeed and then scale proportionately.

It’s really tough time – Out of Sync, Out of Luck

Timing can easily make or break a company with enterprise sales. Do you know this that bad timing in the B2B sales process can stalk from various factors? Below, you can find three common timing issues:

Issue no 1: You are behind the market, demanding curve with a not-exponentially-better product and losing to competitors or incumbents.

In case if you don’t give customers enough reasons to make a change, you will meet with too much apathy to close business. However, the default action of enterprise customers is to stick with the current solution.

Make sure that you being little hostile when you solve this problem by uncovering unique and urgent requirements and then believably addressing those. Additional target market focus and customer-driven product iteration can help you out in moving ahead of the curve.

Issue no 2:

You are misery under the weight of a long sales cycle and not closing enough deals instantly. Now, SaaS products and business models have condensed sales cycles in some sectors for various purchases in recent years. For all purchases, this is not true in all enterprise markets; therefore, comprising big purchases in regulated industries or many Global 2000 companies.

Do You Know How You Can Accelerate Slow Sales Cycles?

Have you ever though what to do about those long and expensive sales cycles? “Selling to the consumer is all about selling positive emotions. Selling to the enterprise is about suppressing negative emotions,” says GoodData CEO Roman Stanek.

“Enterprise IT is not a culture of early adopters.” It is true whatever he said; however, there are some practical strategies, which can get slow-moving targets to move. Retaining best practices with sales and marketing processes is very much critical as many global companies have multiple groups of decision-makers, comprising IT gatekeepers, administrative, product users, executive groups, etc.

In reality, even if your product is B2B, possibly one of the biggest mistakes that companies or individuals forget is that their sales process is still P2P. While executives are making decisions for all types of reasons, which are not based simply on product features.

With big sales cycles, developing the link between different levels in the customer organization chart is judgmentally significant. Learning these softer P2P skills, which can help you out in increasing successful triangulation and back-channeling that mainly direct to more enterprise sales.

Prisma – A First Photo Editing App That Uses Neural Networks & Artificial Intelligence

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Recently, I came to know about the most interesting and impressive image editing application ‘Prisma’ that has hit the Apple App Store.

The application can make the images look like paintings from some of the most renown artists of all the time, including Picasso, Van Gogh, Levitan and more.

Today, while going home, I tried my hands on this application and checked some of the excellent filters and got amazing results that you can see in the below image.

prisma_blog_image

Now, the photo applications have completely changed the way photography is done through mobile phones, especially adding filters. I must say that this application stands out from the rest of editing applications.

As it makes use of a server-side combination of neural networks and artificial intelligence to apply 33 different filters that can be changed in intensity using a sliding scale. Once I applied the filters, I can instantly share them on Instagram, Facebook and other social networking sites using the options that I found in the iOS share sheet.

To enjoy different effects of this application, you just need to choose the photo and select an art filter to be applied. Then, you need to wait for the while as Prisma will work on its algorithmic magic, returning the artificial image within some seconds, giving an option of sharing on social networking.

Developed by Prisma Labs, the application will be available on Android platform as well by the end of this month. So, got an iOS device? Just go ahead and download Prisma application. Maybe you can get some of the filters and share your creations on the social network.

To get more information about this application and how to do editing with it, just go through this blog.

7 Unbelievable Things Exist in 2016 That We Dreamed of Before 3 Years

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From past couple of years, I have noticed that a lot of things have been changed. There are enormous technological things that were hard to imagine in 2013 for all of us, but now all those things are normal. These days, all those innovative things become inherent parts of our daily lives and have affected our lives considerably.

From purchasing groceries things to stay fit to clicking pictures. It becomes much easier for all of us as these technological things has revolutionized our daily lifestyle, enabling us to live quality and comfortable life.

Let’s have an instant look at the 7 most exciting and wonderful things exist in 2016 that we dreamed of in 2013:

Paying Bills Without Using Cards & Cash

Today, mobile wallets are like blessings for lots of people as these are getting huge popularity among the youth in India. Day-by-day, digital payments are moving ahead beyond recharges. Now, they can be used for paying everything from online food delivery to purchasing grocery items or booking a new home.

Fitness Tracking Watch

In the recent times, the smart watches have gained the huge attention of people as these watches can perform anything its developers ruling.

This smart watch can be easily connected to your smartphone and shows mobile notifications, displaying anything from text messages to social notifications and a lot more. These watches even allow owners to make calls and reply to messages using only their voice.

Clicking Picture in Which You are also Present

In the year 2013, selfie was just only a word, but it becomes viral within a year. Selfie pictures are clicked by many business leaders to celebrities to other popular worldwide personalities.

For clicking selfie pictures more accurately and perfectly, selfie stick has been introduced that allows people to position their smartphone beyond arms to click better pictures. However, in the upcoming year, selfie drones are also to become the next big thing.

Having an Experienced Without Being Present There

Computer-generated devices are highly fun-loving goggles. Initially, there were named as a gaming device; however, they have offered a lot of practical uses in the short time in the consciousness. For making the virtual experience more wonderful so that you forget the computer, VR headsets have been introduced.

In 2013, I have not imagined that something like that standing on Mars while wearing some intricate goggles. In addition to this, you can also take virtual tours of the places that you have been thinking for a while.

Downloading Videos at a Quick Speed

Over the last couple of years, download speeds become faster and faster, and we all know that the eventual speed nowadays is 4G technology. It not only increases your upload and download speed, but 4G also reduces the buffering time making streaming HD videos. 4G is 5 times faster than 3G services, so we all can imagine how drastic change it will be.

Talking or Chatting With Computers Through Spoken or Written Text

These days, bots are simply amazing artificial intelligence software that can reverse answer to your questions. Many of us have used chatbots at some point but don’t know about them. Have you ever used Apple’s Siri or Microsoft’s Cortana for asking anything?

Unknowingly, you have conversed with a bot. Recently, Facebook has made its own bot news when it has been declared that the companies will ultimately be able to develop and launch their chatbots into Messenger to connect with customers.

In the upcoming years, bots will make people’s lives a much easier and comfortable, but a lot of things are there that need to be learned.

Sending Picture Messages That Delete Themselves Automatically

In the year 2013, you may take this idea as ridiculous, but it becomes extremely common these days. Among all the youngsters, Snap Chat becomes highly popular, enabling users to send photo message, which automatically delete themselves shortly after being viewed.

The application is kept itself updating and interesting by adding more features like picture filters, stories, videos, etc. Moreover, the application also keeps engaged users with the platforms discover channel that features content from renowned publishers.

Technology is Impacting Where We Live

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While reading news on the web, I came across this news that Governor Jerry Brown signed legislation on Friday, which would modernize the entire path for big-scale development in California.

It fortified the state’s reputation for passing some of the major stringent gun regulations in the country. The legislation has urged the extra supply that will help abate the rising housing costs, which is a good thing.

Ultimately, it is not going to matter much. However, in the upcoming years, a new technology revolution will create a huge shift in where people choose to live and, as an enjoyable byproduct, will solve San Francisco’s real estate crisis, sovereign of any legislation.

Let’s have a look at some of the historical content:

More than 30% of Americans, who lived in cities in 1900, that figured had grown to 81% by 2010. In 1900, follow the jobs as farmers made up almost 40% of the labor force.

Today, it is unevenly 2% clearly as jobs have moved to cities that has developed a main cycle of needing more people to serve people already in those cities. However, people like to live close to where they work. The only thing is there is only so much of San Francisco to go around.

In addition to this, technological advances are making it easily possible to mechanize much of the work that is currently carried out by humans. However, it applies to both blue-collar jobs through the Internet of Things and robotics, and white-collar work that is through artificial intelligence.

The extensive applicability of such kind of technologies has led to broad concern about the destruction of jobs. 47% of jobs in the US could be easily replaced by automated processes in the next two decades as per the 2014 Oxford study.

It has been also noted that modern day technology has always removed the requirement for some types of jobs, but it also creates new ones.

Considered as a complete set of tools, technology can be used in varied ways in boosting efficiency. When it comes to talking the Industrial Revolution, it demolished various jobs, but created many more as well.

It has boosted the aggregate wealth of society and started to develop a middle class, who could enjoy healthy, education and other advantages, which formerly had been obtainable to the wealthiest. It will be quite exciting to forecast the kinds of jobs, which this new revolution is about to create.

Though, 9 out of 10 most-in demand jobs of 2012 did not exist in 2013, suggesting that this latest revolution is generating new employment opportunities. For various people, it is an excessively positive as various new jobs need different skills set, so you cannot turn a meeting plant worker into a data scientist instantly. Across big areas of a complex, interconnected economy, the digital revolution may happen instantly, which is very tight in-built feedback loops.

Rather than having a few densely populated pockets like we are doing today, people will disperse as technology is going to make it a lot simpler to do so, and it will be affordable to live. Technology will change where we live – not just in USA or San Francisco, buy in every major city.

Exploiting machine learning in cybersecurity

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Automation is future.

Machine learning is a method of data analysis that automates analytical model building.

Fraud detection, Web search results, Real-time ads on web pages and mobile devices, Prediction of equipment failures, Network intrusion detection,Pattern / image recognition and many of our day-to-day activities are powered by machine learning algorithms.

Thanks to technologies that generate, store and analyze huge sets of data, companies are able to perform tasks that previously were impossible. But the added benefit does come with its own setbacks, specifically from a security standpoint.

With reams of data being generated and transferred over networks, cybersecurity experts will have a hard time monitoring everything that gets exchanged — potential threats can easily go unnoticed. Hiring more security experts would offer a temporary reprieve, but the cybersecurity industry is already dealing with a widening talent gap, and organizations and firms are hard-pressed to fill vacant security posts.

The solution might lie in machine learning, the phenomenon that is transforming an increasing number of industries and has become the buzzword in Silicon Valley. But whilemore and more jobs are being forfeited to robots and artificial intelligence, is it conceivable to convey to machines a responsibility as complicated as cybersecurity? The topic is being hotly debated by security professionals, with strong arguments on both ends of the spectrum. In the meantime, tech firms and security vendors are looking for ways to add this hot technology to their cybersecurity arsenal.

Pipe dream or reality?

Simon Crosby, CTO at Bromium, calls machine learning the pipe dream of cybersecurity, arguing that “there’s no silver bullet in security.” What backs up this argument is the fact that in cybersecurity, you’re always up against some of the most devious minds, people who already know very well how machines and machine learning works and how to circumvent their capabilities. Many attacks are carried out through minuscule and inconspicuous steps, often concealed in the guise of legitimate requests and commands.

Others, like Mike Paquette, VP of Products at Prelert, argue that machine learning is cybersecurity’s answer to detecting advanced breaches, and it will shine in securing IT environments as they “grow increasingly complex” and “more data is being produced than the human brain has the capacity to monitor” and it becomes nearly impossible “to gauge whether activity is normal or malicious.”

Stephan Jou, CTO at Interset, is a proponent of machine-learning-powered cybersecurity. He acknowledges that AI is still not yet ready to replace humans, but it can boost human efforts by automating the process of recognizing patterns.

What’s undeniably true is that machine learning has very distinct use cases in the realm of cybersecurity, and even if it’s not a perfect solution, it is helping improve the fight against cybercrime.

Attended machine learning

The main argument against security solutions powered by unsupervised machine learning is that they churn out too many false positives and alerts, effectively resulting in alert fatigue and a decrease in sensibility. On the other hand, the amount of data and events generated in corporate networks are beyond the capacity of human experts. The fact that neither can shoulder the burden of fighting cyberthreats alone has led to the development of solutions where AI and human experts join forces instead of competing with each other.

MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) has led one of the most notable efforts in this regard, developing a system called AI², an adaptive cybersecurity platform that uses machine learning and the assistance of expert analysts to adapt and improve over time.

The system, which takes its name from the combination of artificial intelligence and analyst intuition, reviews data from tens of millions of log lines each day and singles out anything it finds suspicious. The filtered data is then passed on to a human analyst, who provides feedback to AI²by tagging legitimate threats. Over time, the system fine-tunes its monitoring and learns from its mistakes and successes, eventually becoming better at finding real breaches and reducing false positives.

Research lead Kaylan Veeramachaneni says, “Essentially, the biggest savings here is that we’re able to show the analyst only up to 200 or even 100 events per day,” which is considerably less than the tens of thousands security events that cybersecurity experts have to deal with every day.

The platform was tested during a 90-day period, crunching a daily dose of 40 million log lines generated from an e-commerce website. After the training, AI² was able to detect 85 percent of the attacks without human assistance.

Finnish security vendor F-Secure is another firm that has placed its bets on the combination of human and machine intelligence in its most recent cybersecurity efforts, which reduces the time it takes to detect and respond to cyberattacks. On average, it takes organizations several months to discover a breach. F-Secure wants to cut down the time frame to 30 minutes with its Rapid Detection Service.

The system gathers data from a combination of software installed on customer workstations and sensors placed in network segments. The data are fed to threat intelligence and behavioral analytics engines, which use machine learning to classify the incoming samples and determine normal behavior and identify outliers and anomalies. The system uses near-real-time analytics to identify known security threats, stored data analytics to compare samples against historical data and big data analytics to identify evolving threats through anonymized datasets gathered from a vast number of clients.

At the heart of the system is a team of cybersecurity experts who will go through the results of the machine learning analysis and ultimately identify and handle security incidents. With the bulk of the work being carried out by machine learning, the experts and software engineers can become much more productive and focus on more advanced concepts, such as identifying relationships between threats, reverse engineering attacks and enhancing the overall system.

“The human component is an important factor,” says Erka Koivunen, cybersecurity advisor at F-Secure. “Attackers are human, so to detect them you can’t rely on machines alone. Our experts know how attackers think, the very tactics they use to hide their presence from standard means of detection.”

Sifting through unstructured data

While data gathered from end points and network traffic help in identifying threats, it only accounts for a small part of the cybersecurity picture. A lot of the intelligence and information required to detect and protect enterprises from emerging threats lies in unstructured data such as blog posts, research papers, news stories and social media posts. Being able to make sense of these resources is what gives cybersecurity experts the edge over machines.

Tech giant IBM wants to bridge this gap by taking advantage of the natural language processing capabilities of its flagship artificial intelligence platform Watson. The company intends to take advantage of Watson’s unique capabilities in sifting through unstructured data to read and learn from thousands of cybersecurity documents per month, and apply that knowledge to analyze, identify and prevent cybersecurity threats.

“The fascinating difference between teaching Watson and teaching one of my children,” Caleb Barlow, vice president at IBM Security, told Wired, “is that Watson never forgets.”

Combining this capability with the data already being gathered by IBM’s threat intelligence platform, X-Force Exchange, the company wants to address the shortage of talent in the industry by raising Watson’s level of efficiency to that of an expert assistant and help reduce the rate of false positives.

However, Barlow doesn’t believe that Watson is here to replace humans. “It’s not about replacing humans, but about making them superhumans,” he said in an interview with Fortune.

If the experiment is successful, Watson should deploy to enterprise customers later this year as a cloud service named Watson for Cyber Security. Until then, it has a lot to learn about how cybersecurity works, which is no easy feat.

Cybersecurity startup Massive Alliance uses a slightly different approach to glean information from unstructured data. Its cybersecurity platform Strixususes a set of sophisticated proprietary tools that anonymously gather data related to its customers from the surface web (public search engines), deep web (non-indexed pages) and dark web (TOR-based networks).

The collected data is analyzed by a sentiment-based machine learning engine that discerns the general emotion of content. The mechanics behind the technology include mathematical engines that produce adaptive models of behavior of threat actors and determine the danger they pose against the client. The results are finally submitted to analysts who process the information and spot potential risks.

This technique gives the cybersecurity firm the unique ability to monitor billions of results on a daily basis, identify and alert about the publication of potentially brand-damaging information and proactively detect and prevent attacks and data loss before they happen.

“To date, human intelligence is still the most pointed form of intelligence and can be the most effective in a specific operation or crisis,” says Brook Zimmatore, the company’s CEO. “However, focus on Machine Learning technology across any industry is vital as human efforts have their limitations.”

Will artificial intelligence replace cybersecurity experts?

It’s still too early to determine whether any of these efforts will result in cybersecurity experts being totally replaced by machine-learning-based solutions. Maybe the balance will shift in the future, but, for the moment, humans and robots have no other choice than to unite against the ever-increasing threats that lurk in cyberspace.

Source by Techcrunch