Karl Zimmerman started his web hosting company in 1998 at the age of 14 years old in his bedroom in Fond du Lac, Wisconsin as a hobby to feed Karl’s passion for internet technology. While attending Northwestern University in Evanston, IL, that company evolved from a hobby to a multimillion dollar IT company that today provides thousands of clients with flexible cloud environments, infrastructure hosting, and a full suite of reliable managed and security services. Through it all, Karl’s passion for technology remains, serving as the cornerstone of Steadfast’s principles aimed at helping customers achieve success.

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Jim leads Wasabi’s product management, sales engineering, and customer support efforts. As a veteran of multiple startups and large enterprise organizations, Jim has deep experience in developing, delivering, and supporting products and services that provide security, scalability, and reliability to customers and partners.

Prior to Wasabi, Jim served in product management and sales/support engineering roles at Oracle (via Acme Packet and Covergence), Ciena (via WaveSmith Networks), Lucent Technologies (via Ascend Communications and Cascade Communications) and GTE. Jim holds a BSEE from Northeastern University and a MBA from Babson College.

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Google Cloud has announced the launch of a managed cloud-hosted hardware security module (HSM) service – joining Amazon Web Services and Microsoft Azure in this security benchmark.

The Cloud HSM will enable customers to host encryption keys and perform cryptographic operations in FIPS 140-2 Level 3 certified HSMs, according to a company blog post.

To put this in perspective, the highest level for the FIPS 140-2 standard is Level 4, which aims to “provide a complete envelope of protection around the cryptographic module with the internet of detecting and responding to all unauthorised attempts at physical access.” Level 3, instead, requires “a high probability of detecting and responding to attempts at physical access, use or modification of the cryptographic module.”

Cloud HSM is tightly integrated with Google’s Cloud Key Management Service (KMS), which enables data protection in services such as BigQuery, Google Compute Engine, Google Cloud Storage and DataProc with a hardware-protected key.

The move came about, according to product manager Il-Sung Lee, because customers wanted more options to protect sensitive information and meet compliance mandates. This is despite Google claiming to be the only cloud provider that encrypts all customer data at rest.

“For those of you managing compliance requirements, Cloud HSM can help you meet regulatory mandates that require keys and crypto operations be performed within a hardware environment,” wrote Lee. “In addition to using FIPS 140-2 certified devices, Cloud HSM will allow you to verifiably attest that your cryptographic keys were created within the hardware boundary.”

Some may consider that this has been a long time coming for Google; Microsoft announced Azure Key Vault, a cloud-hosted HSM-backed service for managing cryptographic keys, as far back as the start of 2015. AWS’ CloudHSM tool is also widely documented.

Yet Google’s cloud operations have certainly been innovative elsewhere of late. Earlier this month the company announced the launch of pre-packaged AI services, around contact centres and talent acquisition, as well as supporting NVIDIA’s Tesla P4 GPUs, for graphics-intensive and machine learning applications.

Find out more about Google Cloud HSM beta here.

Microsoft continues to hold a solid lead in the enterprise SaaS market ahead of Salesforce – but even though the market can be considered mature there is plenty more potential to come.

That is the key finding from the latest note by Synergy Research. The analysis, which comes from Q2 data, notes that the enterprise SaaS market is new generating $20 billion in quarterly revenues for vendors. Microsoft holds 17% of the overall market, with Salesforce the nearest contender and the only other company to hold more than 10% share.

Microsoft is also the fastest growing company with 45% annual growth. Oracle, with just over 5% overall share, saw 43% growth, with SAP (36%), Adobe (32%) and Salesforce (25%) all solid.

While everyone is seeing solid growth, it also plays into the fact that the market is somewhat fragmented with different areas for growth. Per the company’s figures from this time last year, Microsoft, Cisco and Google dominate collaboration, with Salesforce, Microsoft and Zendesk in CRM and Oracle, SAP and Infor for ERP.

“There is a fascinating battle for SaaS playing out, with traditional enterprise software vendors slugging it out with born in the cloud vendors like Workday, Zendesk, ServiceNow and Dropbox,” said John Dinsdale, a chief analyst at Synergy. “The latter group are helping to rapidly transform the market, but the more traditional players like Microsoft, SAP, Oracle and IBM still have a huge base of on-premise software customers that they can convert to a SaaS-based consumption model.

“Meanwhile Cisco and Google too are making ever-bigger inroads into the SaaS market, via Cisco’s collaboration apps and software vendor acquisitions and Google’s G Suite,” added Dinsdale.

Looking at the most recent figures, IBM last month announced a third consecutive quarter of revenue growth, with cloud revenue up 20% and representing almost a quarter of the company’s total revenue. From SAP’s perspective, the company raised its 2020 outlook, having previously praised ‘stellar cloud bookings’ earlier this year.

It’s a sad fact of life that whenever someone owns anything of value, there’s someone else out there who wants to get their hands on it illegally. Today’s corporate crown jewels are the critical data on which organisations depend and the highwaymen are cybercriminals, who have built a lucrative industry from ransomware attacks that disrupt businesses, steal data and aim to extract payment from their victims.

Tackling this scourge is a critical challenge for IT managers on several levels, but when it comes to the crunch, putting solid cloud backup and disaster recovery (DR) plans in place can help businesses keep hold of the aces and hang on to their crown jewels.

Ransomware is on the rise again and it’s getting smarter

Recent figures show that, after a slight lull towards the end of 2017, ransomware attacks have once again accelerated in the first half of 2018, reaching a reported 181.5 million incidents. This rise has been driven by the emergence of ransomware-as-a-service, which now means that almost zero technical expertise is needed to perpetrate an attack – just a target and a willing ransomware provider. 

As well as increasing in volume, attacks are also evolving to become more sophisticated, seeking out and encrypting remote network drives and servers and hunting down and removing shadow copies and backup files. The rationale behind this evolution is simple: to lock down the victim’s recovery options and increase the chances of a ransom being paid.

This alteration in tactics, combined with the risks of business disruption, financial loss and reputational damage associated with cyberattacks, means that IT managers are under greater pressure than ever as they strive to defend against ransomware. And there’s no silver bullet. The various attack vectors and strategies employed by adversaries means that a multi-layered approach is needed, requiring IT managers to wear a number of hats, from psychologist to detective to – in the final event – emergency services.

The psychologist

A large proportion of ransomware is launched via the actions of an innocent user who trustingly clicks on apparently genuine emails, links and websites. User training that helps employees understand the psychology of ransomware and the tricks attackers might use to target them is the first line of defence for businesses. Awareness of ransomware among the public has increased, partly due to the high profile Wannacry infection, but at the same time social engineering and phishing techniques have grown more sophisticated, so it’s important to keep users up to date and alert to the ways they could become vulnerable.

The detective

In an ideal world, users wouldn’t be exposed to ransomware attempts in the first place, and that’s where prevention and protection comes in. By ramping up endpoint detection capabilities, ensuring newly identified vulnerabilities are quickly patched and operating robust anti-virus and anti-malware software, businesses can detect and mitigate attacks before they can do any harm.

The emergency services

Despite these defensive tactics, the sheer volume and growing sophistication of attacks means businesses need to assume it’ll be a case of when, not if, an attack makes it through. A solid emergency response plan is essential. Three key tools, used in conjunction, can bolster the company’s arsenal, ready to swing into action in the event of a successful attack, to protect access to the organisation’s most valuable data and restore operations with minimal disruption:

• Snapshots: A SAN/NAS-based snapshot is effectively a point in time image of your data. Snapshots can be programmed into the routines of practically any application or storage device and are completed isolated from the data itself, so there’s no way malicious code – whatever its level of sophistication – can detect or remove them. 
• Backups: There are a raft of important reasons why businesses should use back-up in ordinary operations, but it is also a great place to have up your sleeve when you want to avoid paying the ransom and instead recover your data from your own sources. Follow the 3-2-1 rule: three copies of your data, on two different media types, with one copy off-site in the cloud. This off-site copy is your insurance policy. It’s “air-gapped” from the business so there is no way that it can be compromised by malicious code that seeks to delete or encrypt locally hosted or networked back-up files.
• Disaster recovery: While it’s not a flood or a fire, a successful ransomware attack could be just as devastating for your business. In fact, given the volume of attacks in progress right now (figures suggest that a company is hit by ransomware every 40 seconds), you’re actually far more likely to find yourself with a ransomware disaster on your hands. With disaster recovery set up in the cloud, you can have your systems back up and running in that environment right back to the moment that the attack locked the system. This isolates your data from the event and minimises both recovery time and data loss – mitigating both the hard and soft costs of system outages and data breach.

Internal and external security threats to companies are occurring with increased regularity, with malware and viruses a constant challenge. This is why companies need a recovery solution that mitigates the risk of critical data being lost or destroyed, in the event of a breach, that can easily restore mailboxes to an instance before the attack. Backing up your data would be quite a long process if it had to be done manually. Fortunately, over the years, CSP providers like iland have adapted their solutions so they can be included directly in widely-used software suites such as Microsoft Office 365. This means that by automatically backing up your data once a day, the solution eliminates the risk of losing access to and control over Office 365 suite data including mail, SharePoint and OneDrive – so that users’ data is always hyper-available and protected, therefore avoiding any major disruption to your business.

The layered defence approach should also be applied to backup and recovery. The structure of that strategy revolves around classifying the value of your different data or application tiers and establishing your appetite for disruption for each tier. If you only back up your data overnight, say at 7.00pm, and the ransomware attack takes place at 6.45pm, your business loses a whole day of data. Is that acceptable? If not, you need to modify your schedules to match your risk appetite for the different classifications of data.

Testing is critical. If you don’t test your emergency plan regularly, how do you know it will work when it matters? It should be possible to fully test without interrupting the normal flow of business. It’s also worth remembering that ransomware attacks (and indeed other kinds of disaster) don’t happen quarterly, or during office hours, so your testing schedule needs to reflect the real world rather than an artificial timeframe to offer you the best information about the security performance of your system. Finally, take advantage of your cloud provider’s expertise and get them to advise you on the right kind of set-up for your needs – that’s what they’re there for.

Ransomware looks likely to remain the bane of the IT department for the foreseeable future and with attacks growing more sophisticated, it’s time to put cloud-based backup and disaster recovery in place to safeguard your data crown jewels and keep your business up and running.

Capitalising on new digital business models and the growth opportunities they provide are forcing companies to re-evaluate ERP’s role. Made inflexible by years of customisation, legacy ERP systems aren’t delivering what digital business models need today to scale and grow.

Legacy ERP systems were purpose-built to excel at production consistency first at the expense of flexibility and responsiveness to customers’ changing requirements. By taking a business case-based approach to integrating Artificial Intelligence (AI) and machine learning into their platforms, Cloud ERP providers can fill the gap legacy ERP systems can’t.

Closing legacy ERP gaps with greater intelligence and insight

Companies need to be able to respond quickly to unexpected, unfamiliar and unforeseen dilemmas with smart decisions fast for new digital business models to succeed. That’s not possible today with legacy ERP systems. Legacy IT technology stacks and the ERP systems they are built on aren’t designed to deliver the data needed most.

That’s all changing fast. A clear, compelling business model and successful execution of its related strategies are what all successful Cloud ERP implementations share. Cloud ERP platforms and apps provide organisations the flexibility they need to prioritise growth plans over IT constraints. And many have taken an Application Programming Interface (API) approach to integrate with legacy ERP systems to gain the incremental data these systems provide. In today’s era of Cloud ERP, rip-and-replace isn’t as commonplace as reorganising entire IT architectures for greater speed, scale, and customer transparency using cloud-first platforms.

New business models thrive when an ERP system is constantly learning. That’s one of the greatest gaps between what Cloud ERP platforms’ potential and where their legacy counterparts are today. Cloud platforms provide greater integration options and more flexibility to customise applications and improve usability which is one of the biggest drawbacks of legacy ERP systems. Designed to deliver results by providing AI- and machine learning insights, Cloud ERP platforms, and apps can rejuvenate ERP systems and their contributions to business growth.

The following are the 10 ways to improve cloud ERP with AI and machine learning, bridging the information gap with legacy ERP systems:

Cloud ERP platforms need to create and strengthen a self-learning knowledge system that orchestrates AI and machine learning from the shop floor to the top floor and across supplier networks

Having a cloud-based infrastructure that integrates core ERP web services, apps, and real-time monitoring to deliver a steady stream of data to AI and machine learning algorithms accelerates how quickly the entire system learns. The cloud ERP platform integration roadmap needs to include APIs and web services to connect with the many suppliers and buyer systems outside the walls of a manufacturer while integrating with legacy ERP systems to aggregate and analyse the decades of data they have generated.

Virtual agents have the potential to redefine many areas of manufacturing operations, from pick-by-voice systems to advanced diagnostics

Apple’s Siri, Amazon’s Alexa, Google Voice, and Microsoft Cortana have the potential to be modified to streamline operations tasks and processes, bringing contextual guidance and direction to complex tasks. An example of one task virtual agents are being used for today is guiding production workers to select from the correct product bin as required by the Bill of Materials. Machinery manufacturers are piloting voice agents that can provide detailed work instructions that streamline configure-to-order and engineer-to-order production. Amazon has successfully partnered with automotive manufacturers and has the most design wins as of today. They could easily replicate this success with machinery manufacturers.

Design in the Internet of Things (IoT) support at the data structure level to realise quick wins as data collection pilots go live and scale

Cloud ERP platforms have the potential to capitalise on the massive data stream IoT devices are generating today by designing in support at the data structure level first. Providing IoT-based data to AI and machine learning apps continually will bridge the intelligence gap many companies face today as they pursue new business models. Capgemini has provided an analysis of IoT use cases shown below, highlighting how production asset maintenance and asset tracking are quick wins waiting to happen. Cloud ERP platforms can accelerate them by designing in IoT support.

Reducing equipment breakdowns and increasing asset utilisation by analysing machine-level data to determine when a given part needs to be replaced

It’s possible to capture a steady stream of data on each machine’s health level using sensors equipped with an IP address. Cloud ERP providers have a great opportunity to capture machine-level data and use machine learning techniques to find patterns in production performance by using a production floor’s entire data set. This is especially important in process industries where machinery breakdowns lead to lost sales. Oil refineries are using machine learning models comprise more than 1,000 variables related to material input, output and process perimeters including weather conditions to estimate equipment failures.

Designing machine learning algorithms into track-and-traceability to predict which lots from which suppliers are most likely to be of the highest or lowest quality

Machine learning algorithms excel at finding patterns in diverse data sets by continually applying constraint-based algorithms. Suppliers vary widely in their quality and delivery schedule performance levels. Using machine learning, it’s possible to create a track-and-trace application that could indicate which lot from which supplier is the riskiest and those that are of exceptional quality as well.

AI and machine learning can provide insights into how Overall Equipment Effectiveness (OEE) can be improved that aren’t apparent today

Manufacturers will welcome the opportunity to have greater insights into how they can stabilise then normalise OEE performance across their shop floors. When a cloud ERP platform serves as an always-learning knowledge system, real-time monitoring data from machinery and production assets provide much-needed insights into areas for improvement and what’s going well on the shop floor.

Cloud ERP providers need to pay attention to how they can help close the configuration gap that exists between PLM, CAD, ERP and CRM systems by using AI and machine learning

The most successful product configuration strategies rely on a single, lifecycle-based view of product configurations. They’re able to alleviate the conflicts between how engineering designs a product with CAD and PLM, how sales & marketing sell it with CRM, and how manufacturing builds it with an ERP system. AI and machine learning can enable configuration lifecycle management and avert lost time and sales, streamlining CPQ and product configuration strategies in the process.

Improving demand forecasting accuracy and enabling better collaboration with suppliers based on insights from machine learning-based predictive models is attainable with higher quality data

By creating a self-learning knowledge system, cloud ERP providers can vastly improve data latency rates that lead to higher forecast accuracy. Factoring in sales, marketing, and promotional programs further fine-tunes forecast accuracy.

Implementing self-learning algorithms that use production incident reports to predict production problems on assembly lines needs to happen in cloud ERP platforms

A local aircraft manufacturer is doing this today by using predictive modeling and machine learning to compare past incident reports. With legacy ERP systems these problems would have gone undetected and turned into production slowdowns or worse, the line having to stop.

Improving product quality by having machine learning algorithms aggregate, analyse and continually learn from supplier inspection, quality control, Return Material Authorisation (RMA) and product failure data

Cloud ERP platforms are in a unique position of being able to scale across the entire lifecycle of a product and capture quality data from the supplier to the customer. With legacy ERP systems manufacturers most often rely on an analysis of scrap materials by type or caused followed by RMAs. It’s time to get to the truth about why products fail, and machine learning can deliver the insights to get there.

Keeping an application running at scale can be a daunting task. When do you need to add more capacity? Larger databases? Additional servers? These questions get harder as the complexity of your application grows. Microservice based architectures and cloud-based dynamic infrastructures are technologies that help you keep your application running with high availability, even during times of extreme scaling.

But real cloud success, at scale, requires much more than a basic lift-and-shift migration. It requires successfully navigating the world of the dynamic cloud. The dynamic cloud doesn’t just let you scale your applications, but it makes the process much faster and easier. It also allows your development teams to respond to changes faster, and implement these changes faster. Not everyone is ready for moving their applications to a fully dynamic cloud-based environment, but success in the cloud requires it.

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