These ten enterprise M&A deals totaled over $40B in 2019

It would be hard to top the 2018 enterprise M&A total of a whopping $87 billion, and predictably this year didn’t come close. In fact, the top 10 enterprise M&A deals in 2019 were less than half last year’s, totaling $40.6 billion.

This year’s biggest purchase was Salesforce buying Tableau for $15.7 billion, which would have been good for third place last year behind IBM’s mega deal plucking Red Hat for $34 billion and Broadcom grabbing CA Technologies for $18.8 billion.

Contributing to this year’s quieter activity was the fact that several typically acquisitive companies — Adobe, Oracle and IBM — stayed mostly on the sidelines after big investments last year. It’s not unusual for companies to take a go-slow approach after a big expenditure year. Adobe and Oracle bought just two companies each with neither revealing the prices. IBM didn’t buy any.

Microsoft didn’t show up on this year’s list either, but still managed to pick up eight new companies. It was just that none was large enough to make the list (or even for them to publicly reveal the prices). When a publicly traded company doesn’t reveal the price, it usually means that it didn’t reach the threshold of being material to the company’s results.

As always, just because you buy it doesn’t mean it’s always going to integrate smoothly or well, and we won’t know about the success or failure of these transactions for some years to come. For now, we can only look at the deals themselves.

It’s Beginning to Look a IoT Like Christmas

Tis the season, and as I look at what gifts are trending on various sites, I am amazed at the amount and variety of connected, IoT devices. I also had a personal epiphany when asking family what they were hoping to receive from Santa Claus. Each of my parents wanted a smart photo frame so they could cycle through up to date photos of family and especially the grandchildren. They, for some reason, needed a WIFI enabled display device with the ability to link to the latest cloud photos and social media to display on their desks at work.

This led me to think about how many devices like these will end up in the office in January. Devices to help find keys, wallets and cars, devices to better organize our days and remind us of our loved ones during tough days. Not to mention facilities managers unveiling the new smart coffee machine, vending machine or smart fridge added to the corporate network.

How Do Security Professionals Securely Maintaining IT Hygiene with All These Devices?

As we head for this post-holiday IoT apocalypse, how do security professionals support the advance in productivity, engagement and enjoyment in the workplace whilst safely maintaining IT hygiene and control so not to expose the enterprise to vulnerabilities and disaster? 

The answer lies in three things:

  1. Constantly know what devices are on your network, where and why
  2. Immediately tell the difference between IT, IoT and OT devices
  3. Have confidence that your cyber hygiene process can accommodate this influx

IoT devices, smart devices and industrial control, deliver business growth and profitability, but there is no real way to secure them using traditional means. Because these devices often fly under the radar of your traditional control, device security, vulnerability management and IT hygiene, “point in time” scans will no longer suffice and gaining this awareness and inventory through manual processes is simply impossible.

It is also important to note that the existing scanning methods may be too heavy for some devices and may harm these devices which may actually turn out to be critical OT devices.

To avoid mistakes like these you need the ability to fingerprint different devices on your network to tell whether they are smartphones, Ip enabled cameras or critical Industrial control devices. This also allows you to understand the risk associated with their capabilities and whether you can bring them into management, add security software and scan for significant vulnerabilities ripe for exploiting. 

Once you have visibility of your network, you know who’s who and what’s what. You can now assess the potential risks associated with decisions and you can create or easily review good cyber hygiene policies. You understand your estate and you can implement and adjust network segmentation. 

What if this could include AI automation to further reduce manual processes? What if this could be achieved without buying extra equipment or agents?

Introducing SentinelOne’s Ranger

Introducing SentinelOne’s Ranger, the industry’s first solution that allows machines to autonomously protect each other and notify security teams of vulnerabilities, rogue devices, and anomalous behaviour.

SentinelOne Ranger uses your managed endpoints to discover and protect other devices.

Your endpoints become environmentally aware and fend off attacks from one another, without human intervention. The technology enables constant environment visibility with fingerprinting, profiling and categorization of devices at discovery. It uses AI to monitor and control the access of every IoT device and enable immediate action, ultimately solving a problem that has been previously impossible to address at scale.

Want to learn how? Read our datasheet

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InsightFinder gets a $2M seed to automate outage prevention

InsightFinder, a startup from North Carolina based on 15 years of academic research, wants to bring machine learning to system monitoring to automatically identify and fix common issues. Today, the company announced a $2 million seed round.

IDEA Fund Partners, a VC out of Durham, N.C.,​ led the round, with participation from ​Eight Roads Ventures​ and Acadia Woods Partners. The company was founded by North Carolina State University professor Helen Gu, who spent 15 years researching this problem before launching the startup in 2015.

Gu also announced that she had brought on former Distil Networks co-founder and CEO Rami Essaid to be chief operating officer. Essaid, who sold his company earlier this year, says his new company focuses on taking a proactive approach to application and infrastructure monitoring.

“We found that these problems happen to be repeatable, and the signals are there. We use artificial intelligence to predict and get out ahead of these issues,” he said. He adds that it’s about using technology to be proactive, and he says that today the software can prevent about half of the issues before they even become problems.

If you’re thinking that this sounds a lot like what Splunk, New Relic and Datadog are doing, you wouldn’t be wrong, but Essaid says that these products take a siloed look at one part of the company technology stack, whereas InsightFinder can act as a layer on top of these solutions to help companies reduce alert noise, track a problem when there are multiple alerts flashing and completely automate issue resolution when possible.

“It’s the only company that can actually take a lot of signals and use them to predict when something’s going to go bad. It doesn’t just help you reduce the alerts and help you find the problem faster, it actually takes all of that data and can crunch it using artificial intelligence to predict and prevent [problems], which nobody else right now is able to do,” Essaid said.

For now, the software is installed on-prem at its current set of customers, but the startup plans to create a SaaS version of the product in 2020 to make it accessible to more customers.

The company launched in 2015, and has been building out the product using a couple of National Science Foundation grants before this investment. Essaid says the product is in use today in 10 large companies (which he can’t name yet), but it doesn’t have any true go-to-market motion. The startup intends to use this investment to begin to develop that in 2020.