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Splunk 2017 Predictions: What's Next for Cloud, IT, DevOps, Machine Learning and Cybersecurity

VMblog Predictions 2017

Virtualization and Cloud executives share their predictions for 2017.  Read them in this 9th annual VMblog.com series exclusive.

Contributed by Praveen Rangnath, Rick Fitz, Toufic Boubez and Haiyan Song of Splunk

What's Next for Cloud, IT, DevOps, Machine Learning and Cybersecurity

Praveen Rangnath, Sr. Director of Cloud Marketing at Splunk

Praveen Rangnath 

  • Building from the cloud up - Organizations will begin to see cloud not only as their infrastructure of choice, but will embrace it as a foundational driver of innovation (cloud-native).
  • No more cloud price wars - The race to zero is over. 2017 will be less about cloud price and more about agility and innovation.
  • Different cloud strategies normalize - we'll see more vendors deploy cloud-agnostic services that can work across all cloud providers and strategies. We will see more companies investing in long term strategic cloud partnerships, ensuring their partners embrace others in the cloud ecosystem to ensure delivering powerful solutions for customers.
  • Cloud providers harness IoT - Today's "smart home" concept will grow to become a smart enterprise, powered by the cloud. Connected technologies, from voice-recognition services such as Amazon Alexa to thermostats like Nest, will start seeing enterprise adoption.
  • Traditional industries and cloud vendors finally meet halfway - While there will always be some level of skepticism among them, their lack of trust is decreasing
  • Pace of innovation is unpredictable - The next big innovation from the cloud will be something that no one today is predicting.

Rick Fitz, SVP of IT Markets, Splunk

Rick Fitz 

  • Analytics go mainstream - In 2017, we will see a major focus on analytics, with more IT professionals and engineers relying on emerging technologies like machine learning, automation and predictive analytics to do higher level work behind the scenes.
  • Platform as a service (PaaS) enters the big stage - IaaS was a $10 billion set of training wheels for cloud - once you take those training wheels off, what happens? Now PaaS offerings will become real and cloud native.
  • Containers go big - Containers are bringing to light an entirely new IT stack, compartmentalizing it into its smallest forms. Once IT professionals gain visibility across their entire IT environment and manage it appropriately, those benefits translate into real business value. In 2017, container management and orchestration will come to the forefront.
  • DevOps get C-level attention - We are seeing a broader transition happening within DevOps: moving from the mindset of an artisanal workshop to an IT factory. We'll see c-suite executives get involved in DevOps oversight and company-wide implementation.
  • Visibility Across DevOps for the Good, the Bad and the Ugly - In the 2017, we will see more visibility and transparency across DevOps. DevOps is "growing up".
  • Vendor consolidation - The sea of DevOps vendors will begin to shrink in 2017. As long as these solutions remain fragmented, DevOps won't get the market share it needs for broad adoption.
  • "Shift Left" within DevSecOps strengthens - With complex cyber threats going mainstream in 2016, 2017 will see the end user demand proactive integration of security and compliance into the integrity of apps from first stages of agile development.

Toufic Boubez, VP Engineering, Splunk

Toufic Boubez 

  • Machine learning-washing - Expect the market to be flooded with solutions that promise machine learning capabilities and grab headlines, but deliver no substance. There will undeniably be valuable machine learning solutions in the market, but it will become more difficult to separate the signal from the noise. It's going to be a confusing year for companies attempting to parse through this noise and deploy real machine learning capabilities that help them achieve business goals.
  • The appification of machine learning - Although there will be a lot of overpromising and overuse of the term "machine learning," there will also be some real value gained from machine learning under the hood of advanced applications. Machine learning will become more accessible to a broader user base and applied more broadly to standard IT and business activities through both turnkey and invisible application integration. Machine learning capabilities will start infiltrating enterprise applications, and advanced applications will provide suggestions --  if not answers -- and provide intelligent workflows based on data and real-time user feedback. This will allow business experts to benefit from customized machine learning without having to be machine learning experts.
  • Predictive maintenance - Predictive analytics is beginning to evolve into preventive analytics in the enterprise, but for industrial environments predictive analytics is king. In 2017, industries will leverage machine learning to execute predictive maintenance. As automation is used to quickly and efficiently ensure business continuity, enterprises will turn to machine learning to up the ante.
    • The biggest industry opportunity for this will be across industrial environments. In a sector where money, efficiency and safety is reliant on machines, the industrial industry will gain deeper performance insights from machine learning. By predicting maintenance cycles, enterprises can reduce machinery failure and error, and also save costs by limiting costly maintenance on less frequently used machines.
  • Deep learning creeps into the enterprise - While machine learning and AI are still relatively new in the enterprise, we can expect deep learning to start making an appearance. With deep learning, more layers of processing elements are added for the ability to aggregate not only textual data, but also more complex data like voice and sensor data into meaningful patterns. Enterprises will start experimenting with deep learning for a greater range of activities. Larger sample sizes for data will continue to increase accuracy for recommendations and predictions, especially when it comes to potential infrastructure outages.
    • In the longer run, advanced learning capacities allow all data to become part of a neural network fabric, expanding beyond internal data and encompassing higher-level data from outside sources. Much like the mandated cybersecurity information sharing acts, such as the Cybersecurity Information Sharing Act, deep learning will help set the stage for organizations to create and use neural networks.

Haiyan Song, SVP of Security Markets, Splunk

Haiyan Song 

  • Internet is a critical infrastructure - DDoS attacks like the Mirai botnet powered on Dyn have shown us that the foundation of our connected world, the internet, is tremendously vulnerable. As such, the internet must be treated as a critical infrastructure. What does this mean? The internet must be continually monitored and well protected from debilitating attacks. The internet needs to be more resilient. As a society, we're as dependent on internet as electricity. This issue carries even more importance in industries like healthcare and the government, for which downtime could mean life or death and national security. 
    • To ensure systems stay online at all times, we'll see more companies start to talk about and place emphasis on understanding what they need to detect-not just preventing attacks. This shift in mentality in which detection is valued over prevention has been in the works for years, but 2017 is the year the industry finally starts to turn over a new leaf. Attacks are unavoidable, but protecting critical assets and infrastructure (like the internet) through detection-centric technology will be the pursuit of cyber security professionals. 
  • Machine Learning, Behavioral Analytics and Adaptive Response come front & center - It's clear that hackers have refined their art, and are outpacing enterprise security defenses. At the same time, more organizations are adopting an analytics-driven approach to security, leveraging machine learning and enabling adaptive response, which encourages automating retrieval, sharing and response in multi-vendor environments. In 2017, we expect to see adoption of both increase to further make inroads against the bad guys.
    • Machine learning based solutions and Runbook automation will become more mainstream in 2017 as companies seek to become smarter and faster to identify and respond to threats. An example of this is behavioral analytics, which will allow companies to apply more data and automation techniques to monitor and verify identities, API requests, machine-to-machine interactions, and signal anomalies that could be a security threat. Machine learning that is built into core platforms (like SIEM) is the next evolution, allowing greater flexibility and better efficiency in threat investigations, risk management and incident response. 
  • A ransomware marketplace is emerging - In 2017, we'll see ransomware being commoditized and democratized in dark web marketplaces. This is a real threat because it shows that nefarious entities and cybercrime syndicates around the world are working together to establish structure and a value chain for ransomware tools, which will generate greater profits. In effect, this marks a fast growing underground industry on the dark web in which the makings of cyberattacks can be bought and sold, and profit reinvested to better the tools to generate even bigger returns.
    • To combat this, enterprises are seeking dynamic resources for real-time intelligence that help detect ransomware threats in real-time within their security nerve center. In the meantime, companies need to identify their risk tolerance to place the highest security around their most valuable assets and enterprises needs to revisit their Business Continuity plans to ensure critical IT infrastructure and mission critical data are protected from taken hostage. This approach will increasingly help stop the propagation of ransomware and enable companies to put "bodyguards" arounds the assets that matter most.
  • IoT will be the new favored vector of cyberattacks - Backdoors in IoT infrastructures may provide hackers a gift-millions of unprotected gateways into IT or OT (Operation Technology) systems. Many reports will tell you that large enterprises are already facing hundreds of millions of automated attacks per day, and IoT growth is likely to increase this figure exponentially. With the attack surface both widening and deepening, OT will become a popular and well known acronym just like IT. 
    • The proliferation of IoT devices and its lack of maturity in security design will demand better strategy in enterprise topology, network zoning and operational intelligence to protect the enterprise. As data collection within IoT poses news challenges, we'll see instrumentation, automation and real-time monitoring and response get a lot attention as well. 
  • The Weaponization of Information - Information on your personal desktop enables you to work - but what happens when that information is stolen? Historically, hackers have stolen data and extorted valuable IP or personal information for one reason: monetary gain. But over the past two years, that has changed. Whether it be the DNC hack, the Sony breach, or the more recent hack of AdultFriendFinder, hackers are weaponizing information to damage reputations and brands. Many tools will be needed to combat rogue stealing of secrets for purely malicious reasons, with User Behavior Analytics (UBA) technologies being a key component of the solution. Expect to see further adoption of UBA in 2017 as organizations leverage the real-time insights these technologies provide to learn who has access to their most sensitive information.

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Published Wednesday, December 28, 2016 9:05 AM by David Marshall
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