Cloud access security broker (CASB) Netskope has announced the close of a series F funding round of $168.7 million (£130.5m) to ‘further cement [its] position as the leader in accelerating security transformation throughout the enterprise’, in the company’s words.

The funding round was led by existing investor Lightspeed Venture Partners – whose interests in the cloud security space have included Datrium and Zscaler – alongside investment from Accel, Base partners, Geodesic Capital, Iconiq Capital, Sapphire Ventures and Social Capital.

CEO Sanjay Beri argued in a blog post following the announcement how, without security transformation, digital transformation will fail – and how Netskope is best placed to fulfil organisations’ needs.

“Legacy cyber security vendors have reacted to this [cloud] shift by acquiring companies and cobbling together disparate architectures and products in an attempt to present customers with a ‘unified’ solution, but in reality these products are unified in name only and they were designed for an environment where data and applications were placed in centralised data centres and IT teams were primarily the ones responsible for selecting and deploying applications,” wrote Beri.

“Unlike legacy vendors, Netskope was born in the cloud. We empower our customers to achieve the security transformation they need in order to make the shift to a digital-first model.”

Netskope’s vision is around protecting all assets with a unified SaaS, IaaS and web security platform. Having been purely cloud-focused, the strategic decision was made last year to increase visibility to the entire web. The company was named as a leader in Gartner’s CASB Magic Quadrant earlier this month. Gartner praised Netskope’s comprehensive risk database and access control policies in its analysis, but noted a caution around a ‘minor’ increase in inquiries around installation challenges and service performance.

Speaking to this publication on the occasion of Netskope’s series E funding last June, Beri noted how even traditional cloud laggards, such as healthcare and finance, were key customers. In some aspects, it was an ideal opportunity; these companies did not wish to fall behind in their technology roadmaps, but they were scared stiff around the data security element.

“You think [financial and insurance would] be the laggards but some of the largest financial institutions are Netskope customers, and they’re leveraging cloud not only because their end users want [it], but because it’s a corporate strategy now,” said Beri. “It’s a competitive advantage, if you can leverage these properly.”

Total funding for Netskope now stands at just over $400 million.

Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more.

Bobby Hellard

Clare Hopping

The ubiquity of popular buzzwords or phrases in the technology community brings a certain kind of pressure. If everyone seems to be talking about the importance and transformative potential of an exciting new technology, then as a CIO it’s only natural to want to dive straight in and explore its potential use cases as soon as possible.

This is particularly true of businesses and edge computing. After all, edge implementation can deliver new experiences to the customer, help develop new products and open up new lines of revenue – why wouldn’t you want to get started as quickly as possible?

However, while no-one wants to stand in the way of transformative new technologies, CIOs have to bring clarity and common sense to the conversation – particularly on how edge implementation will affect a business’s security portfolio. The CIO must take a unified approach to data management when it comes to storage, security, and accessibility, ensuring that security is part of the conversation from the start and is given the same amount of attention from the business as all the exciting edge use cases.

Security centrality

Edge creates a whole new set of security challenges for CIOs, who are used to just dealing with the data centre. Under an edge setup, data is processed closer to the source, away from the centralised data centre that is more physically secure.

For CIOs, the task is to build a data management plan that will future-proof against edge expansion – allowing the business to scale up quickly while maintaining security and cost-efficiency

Because of this, it is imperative that CIOs build comprehensive security into any edge implementation proposal from the start. If security is bolted on after the business goals and ambitions of edge have been set, there will undoubtedly be trouble ahead.

The need for processing at the edge comes from the sheer amount of data generated as our connected world expands over the coming years – according to DataAge 2025, a report sponsored by Seagate and conducted by IDC, 90% of the data created in 2025 will require security protection. More data, of course, means more vulnerability – which is why security, with intelligent data storage and data-at-rest encryption at its foundation, has to be at the heart of any business’s edge computing plans. Couple this with the increased physical concerns – more locations means that there are more sites to keep secure – and it’s clear that this is a complex challenge that must be managed methodically. 

Plan for expansion

Implementing edge is all about driving business growth – the new customer experiences and revenue streams that come with it will mean that your business expands and becomes more complex. For CIOs, therefore, the task is to build a data management plan that will future-proof against edge expansion. This plan must allow the business to scale up quickly while maintaining security and cost-efficiency.

While driven by the particular needs of his or her organisation, a CIO’s edge computing strategy will also need to consider internal politics and agendas. A CIO might find themselves, for example, caught between the business’s OT teams – who will be looking for edge computing spend to help them bring powerful real-time analytics to the factory floor or their supply chain technology – and the IT best practices which are more likely to adopt a more cautious approach. CIOs will need to build a strategy that is flexible enough to handle expansion and can deal with the different priorities of different parts of the business, all while keeping a laser focus on security.

Embrace the benefits

However, the architecture inherent in mobile edge applications can make maintaining a tight security set-up easier. For one thing, storing data at the edge can help enterprises better manage their data from a compliance point of view.

A CIO’s edge computing strategy will also need to consider internal politics and agendas

Because edge allows you to avoid much of the data transfer between devices and the cloud, it is possible to filter sensitive information on the device and only transfer the essential data-model building information to the central data centre.

This makes it easier for enterprises to build the security and compliance framework to meet audits and new regulations.

The edge strategist

When it comes to edge implementation, CIOs need to be at the heart of the planning and rollout. Ultimately, they are responsible for the state and security of all the organisation’s data, and, as detailed in DataAge 2025, the data we all generate will increase exponentially in the coming years.  As edge disperses that data across a wider area than ever before, the scope of the CIO role is only going to expand.

As we know, the role of the CIO is always an evolving one as technology changes and opens up new possibilities. While CIOs must tweak the way they work slightly and adapt to a world where data is more distributed, they have the opportunity to be at the heart of the growth of their business and make even more of an impact its future direction.

Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more.

Bobby Hellard

The Oracle and Amazon mud-slinging shows no signs of slowing down. Amazon Web Services (AWS) has claimed its consumer businesses are now fully off Oracle’s data warehouse, with the vast majority of critical system databases running on its own solution by the end of the year.

The source comes right from the top; writing on Twitter, CEO Andy Jassy said Amazon’s consumer arm turned off Oracle on November 1 and moved to Redshift, Amazon’s equivalent data warehousing tool.

Oracle, and in particular co-founder and CTO Larry Ellison, has been taking sideswipes at all things AWS for almost as long as they have been in the cloud game. As this publication put it last month for the Oracle OpenWorld keynote: “Oracle’s autonomous database is certainly Ellison’s favourite topic right now – but bashing the biggest player in cloud infrastructure must rank a close second.”

Increasingly, Ellison had been telling customers, analysts, and anyone else who would listen of the irony that AWS was still a major Oracle customer. Back in December, following the company’s Q2 2018 results, questions turned to customers moving off the platform. Ellison said: “Let me tell you who’s not moving off of Oracle – a company you’ve heard of that gave us another $50 million this quarter. That company is Amazon.

“Our competitors, who have no reason to like us very much, continue to invest in and run their entire business on Oracle.”

Similarly, Oracle’s much-vaunted autonomous database technology makes Amazon look distinctly second best, according to Ellison. At OpenWorld, the Oracle CTO called Amazon’s upcoming offering ‘semi-autonomous’ and described it as akin to a ‘semi-self-driving car.’ “You get in, you drive, and you die,” he told attendees.

Amazon CTO Werner Vogels added to Jassy’s remarks. Writing again on Twitter, Vogels said the company had “moved on to newer, faster, more reliable, more agile, more versatile technology at lower cost and higher scale.”

Vogels had previously taken to Twitter to claim a CNBC article which alleged Amazon’s moving off Oracle caused a debilitating Prime Day outage was inaccurate. The Amazon CTO refuted the article’s accuracy, claiming the internal document from which the story was based was from an unrelated issue. “Clickbait won,” he wrote.

According to the most recent figures from industry watcher Synergy Research, AWS continues to dominate the market holding more than a third (34%) of total share. Microsoft Azure remains a clear second player with 14% of the market, with Google (7%), IBM (7%) and Alibaba (4%) rounding off the top five. The analyst firm noted that while growth rates were starting to plateau, growing at 100% when reaching massive scale was not an option. More importantly, these companies are all still growing and all still increasing market share.

For now, two events later this month will make for interesting viewing from AWS’ side. Black Friday will give Amazon a chance to show off how good its data warehouse is – with hopefully no Prime Day-esque outages to fuel the fire – while AWS re:Invent kicks off in two weeks’ time. Jassy tweeted that his keynote would ‘squeeze four hours of content into less than three.’ Expect a couple of barbed comments about a certain Redwood-based company to still make the cut, though.

Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more.

• 91% of manufacturers are investing in predictive analytics in the next 12 months, and 50% consider artificial intelligence (AI) a major planned investment for 2019 to support their subscription-based business models.
• New subscription business models and smart, connected products are freeing manufacturers up from competing for one-time transaction revenues to recurring revenues based on subscriptions.
• By 2020, manufacturers are predicting 67% of their product portfolios will be smart, connected products according to an excellent study by Capgemini.
• 71% of manufacturers are using automated sensors for real-time monitoring and data capture of a product’s condition and performance, yet just 25% have the infrastructure in place to analyse it and maximise product uptime.

Manufacturers need to break their dependence on just selling products to selling services if they’re going to grow. Smart, connected products with IoT sensors embedded in them are the future of subscription business models and a key foundation of the subscription economy.

Product reliability and uptime help create subscription economies

In a subscription economy world, whoever excels at product reliability and uptime grows faster than competitors and defines the market. Airlines with the highest on-time ratings have designed in reliability and uptime as part of their company’s identity; their DNA is based on these goals. Worldwide Business Research (WBR) in collaboration with Syncron, a global provider of cloud-based after-sales service solutions focused on empowering the world’s leading manufacturers to maximise product uptime and deliver exceptional customer experiences, recently surveyed to see how manufacturers are addressing the reliability and uptime challenges so critical to growing subscription business.

The research study, Maximised Product Uptime: The Emerging Industry Standard provides insights into how manufacturers can improve their after-sales service solutions. A copy of the study can be downloaded here (PDF, 23 pp., opt-in). Please see pages 20 – 23 for additional details on the report’s methodology. WBR and Syncron designed the survey to gain a deep understanding of manufacturers’ ability to deliver on their customers increasing demand for maximised product uptime, surveying 200 original equipment manufacturers (OEMs), with respondents evenly split between the U.S. and European markets, as well as 100 equipment end-users

Key insights from the study include the following:

34% of manufacturers are ready to compete in a subscription economy and have created a service strategy based on maximised product uptime

39% are planning to have one in two years, and 22% are predicting it will be in 2020 or later before they have on in place. Capgemini found that manufacturers’ plans for smart, connected products would extend beyond these projections, making it a challenge of manufacturers to realise the new subscription revenue they’re planning on in the future.

71% of manufacturers are using automated sensors including IoT for real-time monitoring and data capture of a product’s condition and performance, yet just 25% have the infrastructure in place to analyse it and maximise product uptime

51% of manufacturers have systems in place for analysing the inbound data generated from sensors, yet report they still have more work to do to make them operational.  The 25% of manufacturers with systems in place and at scale will have at least an 18-month jump on competitors who are just now planning on how to make use of the real-time data streams IoT sensors provide.

Predicting part failures before they occur (83%), optimising product functionality based on usage (67%), and using stronger analytics to evaluate product performance (61%) matter most to manufacturers pursuing subscription models

Autonomous product operation (56%) and implementing stronger analytics on ROI ( 50%) are also extremely important. These findings further underscore how manufacturers need to design in reliability and uptime if they are going to succeed with subscription-based business models.

91% of manufacturers are investing in predictive analytics in the next 12 months, and 50% consider artificial intelligence (AI) a major planned investment for 2019

Creating meaningful data models from the massive amount of manufacturing data being captured using automated sensors and IoT devices is making predictive analytics, AI and machine learning extremely important to manufacturers’ IT planning budgets for 2019 and beyond. Combining predictive analytics, AI and machine learning to gain greater insights into pre-emptive maintenance on each production asset, installed product or device is the goal. Knowing when a machine or product will most likely fail is invaluable in ensuring the highest uptime and service reliability levels possible.

77% of manufacturers say having an after-sales service model is critical to their customers’ success today

Customers are ready to move beyond the legacy transactional, break-fix model of the past and want a more Amazon-like experience when it comes to uptime and reliability of every device they own as consumers and use at work. Speed, scale and simplicity are the foundational elements of a subscription business model, and the majority of manufacturers surveyed say their customers are leading them into a value-added after-sales service model.

Clare Hopping

Clare Hopping

Chaos engineering can be defined as experiments over a distributed system at scale, which increases the confidence that the system will behave as desired and expected under undesired and unexpected conditions.

The concept was popularised initially by Netflix and its Chaos Monkey approach. As the company put it as far back as 2010: "The Chaos Monkey’s job is to randomly kill instances and services within our architecture. If we aren’t constantly testing our ability to succeed despite failure, then it isn’t likely to work when it matters most  –  in the event of an unexpected outage."

The foundation of chaos engineering lies in controlled experiments; a simple approach follows.

Interim on controlled experiments with control and experimental groups

A controlled experiment is simply an experiment under controlled conditions. Unless necessary, when performing an experiment it is important to only do so one variable at a time, otherwise it would be increasingly difficult to determine what caused the changes in the results.

One type of controlled experiment is the ‘control and experimental’ group experiment. In this kind of experiment a control group is subject to observation with no variables being modified/affected purposefully, and the experimental group will have one variable at a time modified/affected with the consequent observation of the output at that stage.

A simple approach

Defining a steady state: The main focus is to aim for output metrics and not for system behaviour; the goal is to find out if the system can continue to provide the expected service, but not how it is providing that service. It is useful to define thresholds that will make for an easy comparison between the control group and the experimental group. Also, this will allow for automated comparisons as well, which makes comparing large quantity of metrics easier.

Building the hypothesis around control and experimental group: Due to the nature of chaos engineering, which is a mixture between science and engineering, the foundation is built around having two groups; a control group, which will be unaffected by injected events, and an experimental group, which will be the objective of the variable manipulation.

Introducing variables that correspond to undesired/unexpected events: Changing the state of the variables is what makes the experiment, however those variables need to be of significance and within reason; also, it is of utmost importance to change one variable input at a time.

Try to disprove the hypothesis: The purpose of the experiment is not to validate the hypothesis, it is to disprove it; we must not fool ourselves, knowing that we are the easiest to fool.

Production means production

The only way of increasing confidence in a system running in production is to experiment on the system running in production, under live production traffic, which may seem odd at first glance, but it is absolutely necessary.

One important aspect that sometimes goes unnoticed is that we must not attack the point where we know the system will fail; speaking with upper management I have got answers of the like ‘I know that if I unplug the DB the system will break’. Well that is not chaos engineering – that is just plain foolishness. A chaos experiment will inject failure in parts of the system we are confident will continue to provide the service. Be it be failing over, using HA, or recovering, we know that the service to the client will not be disrupted, and we try our best to prove ourselves wrong, so we can learn from it.

It is also absolutely necessary to minimise the impact of the experiment on real traffic; although we are looking for disruption, we are not pursuing interruption or fault SLO/SLI/SLA; it is an engineering task to minimise negative impact.

Interim on the blast radius

Chaos engineering or failure injection testing is not about causing outages, it is about learning from the system being managed; in order to do so, the changes injected into the system must go from small to big. Inject a small change, observe the output and what it has caused. If we have learned something, splendid; if not, we increase the change and consequently the blast radius. Rinse and repeat. Many people would argue that they know when and where the system will go down, but that is not the intention. The intention is to start small and improve the system incrementally. It is a granular approach, from small to large scale.

Automation

The importance of automation is undisputed, more so on these experiments where it is necessary to:

• Be able to rollback fast enough without human interaction or with minimal HI
• Be able to examine a large set of output metrics at first glance
• Be able to pinpoint infrastructure weak spots visually

Other sources and good reads

The basics: https://principlesofchaos.org/
An extended introduction: https://www.gremlin.com/community/tutorials/chaos-engineering-the-history-principles-and-practice/
A big list of resources: https://github.com/dastergon/awesome-chaos-engineering