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The use of multi-cloud has gained enormously in popularity in accurate years, becoming an essential part of day-to-day operations for many businesses. The adoption of such an approach increases agility whilst minimising vendor lock-in, improving disaster recovery and boosting application performance, all while streamlining costs. In a Gartner study, 81% of respondents said they are working with two or more providers while IDC predicts that global spending on ‘whole cloud’ services will reach $1.3 trillion by 2025 as a digital-first economy becomes the future of enterprise.
Yet, data protection issues relating to an increasing reliance on the multi-cloud approach, is of growing concern. This is because multi-cloud in the enterprise often comes about organically to meet evolving requirements, so is not always planned. Departments within an organisation can choose to store data in different clouds, resulting in the creation of complicated silos of data. This decreases visibility and can have profound repercussions when it comes to compliance. But what can be done to address this, and what steps should IT leaders be taking to implement a solution?
Encrypting confidential data
Although a multi-cloud architecture can make data migration easy, managing access to the data and keeping it confidential can be challenging. Regardless of the mode of transfer or method of storage, the key point to remember is that information remains a valuable commodity that is vulnerable at all possible points of connectivity. The most effective methods to address such vulnerability is to consider secure encryption.
Encrypting data both in transit and at rest is critical. For ultra-secure encryption, data should preferably be encrypted with a FIPS certified, randomly generated, AES 256-bit encrypted encryption key. Confidential information stored locally on a computer or hard drive, sent via email or file sharing service, or shared via data transfer in the cloud should equally be securely encrypted. By taking such an approach, ongoing protection is guaranteed, giving IT leaders peace of mind that their information remains confidential.
Centralised remote management
As the use of multi-cloud environments essentially means that sensitive data is stored in silos and transferred across numerous servers, it’s important for security managers to gain a holistic view as to which cloud providers hold which data, where that data is located and who holds access permissions within the organisation. This will enable geo-fencing and time fencing restrictions to be set, filenames to be appropriately encrypted and remote access to be enabled or disabled depending on requirement. Such controls will go a long way towards eliminating unnecessary security risks.
Key management for encrypted information is also important. Authorised users can be given a copy of a physical encrypted encryption key; a randomly generated encryption key stored within a USB module to allow ultra-secure and real-time collaboration in the cloud. Having a key management system in place provides greater control of encryption keys when using a multi-cloud solution, helping to facilitate a more centralised administration and management approach to data security.
Businesses need to have clear processes in place that all employees follow to uphold adherence to data protection regulations, regardless of where they choose to store the data. Security measures must go beyond simple single-factor cloud login credentials to be truly secure. Incorporating multi-factor authentication will help in relation to data protection governance and is an important step in standardising policies, procedures and processes across multiple cloud providers.
If a malicious threat actor obtains a user’s credentials and compromises an account, the breach is likely to remain unnoticed by the cloud service provider who will not be able to tell the difference between a legitimate user and an attacker. Using an encryption key, but keeping the encryption key away from the cloud, increases the number of security measures from just one level of authentication – the cloud account login – to as many five-factors of authentication. The encryption key should itself be encrypted within an ultra-secure Common Criteria EAL5+ secure microprocessor along with a PIN authenticated code.
As more businesses move toward a multi-cloud setup, security leaders should be looking to follow such recommendations; encrypting and centrally managing their data, and then ensuring that multi-factor authentication is employed for further layers of advanced protection while still enabling operatives to share and collaborate in real time. Managing all devices storing the encrypted encryption key, used to access data in the cloud, will provide a more unified administration and monitoring process, an approach which will bring peace of mind and, ultimately, result in safer data.
Learn more about managing and encrypting data in the cloud:
By Matt Hogstrom, Distinguished Engineer
The concept of devices all over the planet being able to access data no matter where it resides is at the core of modern computing. That’s why organizations keep their critical information in multiple environments, including private clouds, on-premises, and on the public cloud. Given this reality, the key for businesses to get the most value from their technology investments lies in achieving the right balance. Broadcom advises business leaders on the hybrid cloud strategies that can best position them for success.
Business success in today’s competitive landscape depends upon the strength, efficiency, and agility of your IT. That’s why just about every company in the world relies on a strategy that integrates multiple platforms – software, hardware, and business applications delivered through cloud. That strategy is hybrid. It offers the best of all worlds with cloud included as a powerful element in the IT arsenal. The beauty of hybrid cloud lies in the principles of open, flexible compute. By integrating disparate platforms, it excels as a utility for driving business workloads in the best way. It allows organizations to leverage and extend current investments with the cloud model to gain new levels of reach and agility.
Organizations have significant investments in apps and data. Hybrid cloud enables them to extend and innovate those investments while still capitalizing on the benefits of their current stacks. In effect, they fuse the agility, flexibility, and ease of the public cloud with the strength and capabilities of private cloud and traditional, on-premises IT.
A hybrid approach gives IT leaders greater opportunities to optimize their operations. For example, they can quickly and easily add more storage and compute power without having to re-architect their existing systems.
So, what does it take to deploy a successful hybrid cloud solution? It comes down to picking the unique capabilities that each platform offers so they can each contribute specific strengths and work in concert with one another.
IT leaders are now strategically architecting hybrid clouds inclusive of mainframes because the processors are so fast, and the architecture is so robust, that they can seamlessly manage data from multiple sources with near-zero latency. For workloads that are “chatty” and do a lot of I/O or require high throughput – or if you need a business-critical level of security – the mainframe brings unmatched strengths to a hybrid cloud environment.
Today, with an approach that fully leverages open APIs, command line interfaces, and other modern open-source technologies – what Broadcom calls an Open-First approach – it’s easier than ever to integrate and extend the mainframe as part of your hybrid cloud. In many cases, going Open-First can also offset the need to rewrite applications or duplicate data.
This “Open-First” approach offers freedom and choice for customers to modernize and deploy hybrid on their own terms and at the rate and pace they are most comfortable with. For example, mainframe application development can incorporate high-value DevOps practices such as code scanning, code reviews, and test automation.
Cloud programmers can develop applications for deployment on the mainframe just like they do on the cloud with a developer experience that is no different than the cloud-native environment they are already familiar with. And the Open-First path provides options that are non-disruptive bycombining new ways of working with established approaches such asBridge for Git and dynamic environments. For business leaders, embracing the flexibility of an Open-First approach is an easy decision. It enables them to maintain their existing workflows while introducing new ones.
Turns out you actually can have your cake and eat it too.
Hybrid cloud is a game changer from an organizational and a development perspective. It effectively allows system architects to place services anywhere rather than forcing them to work just in a single environment. This creates the ability to disaggregate services and deliver better solutions than they could get in a single autonomous package. Think of it as an “a la carte” approach to building solutions.
A great example is mobile apps that require frequent updates/changes to UX to be competitive while the backend is mainframe. Once applications are developed and deployed, Broadcom enables simpler management. Every Broadcom product registers with a central catalog of services. If technology teams want to use a system view service to get information about CPU or a particular set of information on the mainframe, it’s just a REST API call away – and it can be run on a completely different system. In practical terms, this means you can access data instantly no matter where it resides, allowing companies to take full advantage of the speed and power of their mainframes.
A compelling virtue of Broadcom’s Open-First approach is that, by combining new ways of working with established processes, it provides a non-disruptive path to innovation and value. accurate advances in cloud capabilities open up a new range of possibilities to make hybrid development teams more productive. Using the Open Container Initiative (OCI) model to build images and deploy them using Red Hat OpenShift Container Platform provides a clear example.
Other advances provide operations teams across the hybrid cloud with a better understanding of how they can leverage the mainframe for deploying applications and managing them in a consistent way. At the core of these cloud capabilities lies Broadcom’s expertise in infrastructure and storage – specialized skills and solutions that prioritize data in a way designed from their core to drive maximum efficiency.
To learn more about how Broadcom Software can help you modernize, optimize, and protect your enterprise, contact us here.
About the Author:
Matt Hogstrom is a Distinguished Engineer focusing on AIOPs –Automation. As a motivated technologist, he is passionate about embracing new technology and change to deliver innovative solutions. To that end, his role at Broadcom is to simplify automation of z/OS by enabling Broadcom Monitoring and Automation products to be easily consumable as RESTful services. Matt is based in Research Triangle Park, Durham, NC.
In the most simple computer system architecture, all control lies with the CPU (Central Processing Unit). This means not only the execution of commands that affect the CPU’s internal register or cache state, but also the transferring of any bytes from memory to to devices, such as storage and interfaces like serial, USB or Ethernet ports. This approach is called ‘Programmed Input/Output’, or PIO, and was used extensively into the early 1990s for for example PATA storage devices, including ATA-1, ATA-2 and CompactFlash.
Obviously, if the CPU has to handle each memory transfer, this begins to impact system performance significantly. For each memory transfer request, the CPU has to interrupt other work it was doing, set up the transfer and execute it, and restore its previous state before it can continue. As storage and external interfaces began to get faster and faster, this became less acceptable. Instead of PIO taking up a few percent of the CPU’s cycles, a big transfer could take up most cycles, making the system grind to a halt until the transfer completed.
DMA (Direct Memory Access) frees the CPU from these menial tasks. With DMA, peripheral devices do not have to ask the CPU to fetch some data for them, but can do it themselves. Unfortunately, this means multiple systems vying for the same memory pool’s content, which can cause problems. So let’s look at how DMA works, with an eye to figuring out how it can work for us.
At the core of DMA is the DMA controller: its sole function is to set up data transfers between I/O devices and memory. In essence it functions like the memcpy function we all know and love from C. This function takes three parameters: a destination, a source and how many bytes to copy from the source to the destination.
Take for example the Intel 8237: this is the DMA controller from the Intel MCS 85 microprocessor family. It features four DMA channels (DREQ0 through DREQ3) and was famously used in the IBM PC and PC XT. By chaining multiple 8237 ICs one can increase the number of DMA channels, as was the case in the IBM PC AT system architecture. The 8237 datasheet shows what a basic (single) 8237 IC integration in an 8080-level system looks like:
In a simple request, the DMA controller asks the CPU to relinquish control over the system buses (address, data and control) by pulling HRQ high. Once granted, the CPU will respond on the HLDA pin, at which point the outstanding DMA requests (via the DREQx inputs) will be handled. The DMA controller ensures that after holding the bus for one cycle, the CPU gets to use the bus every other cycle, so as to not congest the bus with potentially long-running requests.
The 8237 DMA controller supports single byte transfers, as well as block transfers. A demand mode also allows for continuous transfers. This allowed for DMA transfers on the PC/PC AT bus (‘ISA’).
Fast-forward a few decades, and the DMA controller in the STM32 F7 family of Cortex-M-based microcontrollers is both very similar, but also very different. This MCU features not just one DMA controller, but two (DMA1, DMA2), each of which is connected to the internal system buses, as described in the STM32F7 reference manual (RM0385).
In this DMA controller the concept of streams is introduced, where each of the eight streams supports eight channels. This allows for multiple devices to connect to each DMA controller. In this system implementation, only DMA2 can perform memory-to-memory transfers, as only it is connected to the memory (via the bus matrix) on both of its AHB interfaces.
As with the Intel 8237 DMA controller, each channel is connected to a specific I/O device, giving it the ability to set up a DMA request. This is usually done by sending instructions to the device in question, such as setting bits in a register, or using a higher-level interface, or as part of the device or peripheral’s protocol. Within a stream, however, only one channel can be active at any given time.
Unlike the more basic 8237, however, this type of DMA controller can also use a FIFO buffer for features such as changing the transfer width (byte, word, etc.) if this differs between the source and destination.
When it comes to having multiple DMA controllers in a system, some kind of priority system always ensures that there’s a logical order. For channels, either the channel number determines the priority (as with the 8237), or it can be set in the DMA controller’s registers (as with the STM32F7). Multiple DMA controllers can be placed in a hierarchy that ensures order. For the 8237 this is done by having the cascaded 8237s each use a DREQx and DACKx pin on the master controller.
So far this all seems fairly simple and straight-forward: simply hand the DMA request over to the DMA controller and have it work its magic while the CPU goes off to do something more productive than copying over bytes. Unfortunately, there is a big catch here in the form of cache coherence.
As CPUs have gained more and more caches for instructions and data, ranging from the basic level 1 (L1) cache, to the more accurate L2, L3, and even L4 caches, keeping the data in those caches synchronized with the data in main memory has become an essential feature.
In a single-core, single processor system this seems easy: you fetch data from system RAM, keep it hanging around in the cache and write it back to system RAM once the next glacially slow access cycle for that spot in system RAM opens up again. Add a second core to the CPU, with its own L1 and possibly L2 cache, and suddenly you have to keep those two caches synchronized, lest any multi-threaded software begins to return some really interesting results.
Now add DMA to this mixture, and you get a situation where not just the data in the caches can change, but the data in system RAM can also change, all without the CPU being aware. To prevent CPUs from using outdated data in their caches instead of using the updated data in RAM or a neighboring cache, a feature called bus snooping was introduced.
What this essentially does is keeping track of what memory addresses are in a cache, while monitoring any write requests to RAM or CPU caches and either updating all copies or marking those copies as invalid. Depending on the specific system architecture this can be done fully in hardware, or a combination of hardware and software.
It should be clear at this point that every DMA implementation is different, depending on the system it was designed for and the needs it seeks to fulfill. While an IBM PC’s DMA controller and the one in an ARM-based MCU are rather similar in their basic design and don’t stray that far apart in terms of total feature set, the DMA controllers which can be found in today’s desktop computers as well as server systems are a whole other ballgame.
Instead of dealing with a 100 Mbit Ethernet connection, or USB 2.0 Fast Speed’s blistering 12 Mbit, DMA controllers in server systems are forced to contend with 40 Gbit and faster Ethernet links, countless lanes of fast-clocked PCIe 4.0-based NVMe storage and much more. None of which should be bothering the CPU overly much if it all possible.
In the desktop space, the continuing push towards more performance, in especially gaming has led to an interesting new chapter in DMA, in the form of storage-to-device requests, e.g. in the form of NVidia’s RTX IO technology. RTX IO itself is based on Microsoft’s DirectStorage API. What RTX IO does is allow the GPU to handle as many of the communication requests to storage and decompressing of assets without involving the CPU. This saves the steps of copying data from storage into system RAM, decompressing it with the CPU and then writing the data again to the GPU’s RAM.
Any good and useful feature of course has to come with a few trade-offs, and for DMA that can be mostly found in things like DMA attacks. These make use of the fact that DMA bypasses a lot of security with its ability to directly write to system memory. The OS normally protects against accessing sensitive parts of the memory space, but DMA bypasses the OS, rendering such protections useless.
The good news here is that in order to make use of a DMA attack, an attacker has to gain physical access to an I/O port on the device which uses DMA. The bad news is that any mitigations are unlikely to have any real impact without compromising the very thing that makes DMA such an essential feature of modern computers.
Although USB (unlike FireWire) does not natively use DMA, the addition of PCIe lanes to USB-C connectors (with Thunderbolt 3/USB 4) means that a DMA attack via a USB-C port could be a real possibility.
As we have seen over the past decades, having specialized hardware is highly desirable for certain tasks. Those of us who had to suffer through home computers which had to drop rendering to the screen while spending all CPU cycles on obtaining data from a floppy disk or similar surely have learned to enjoy the benefits that a DMA-filled world with dedicated co-processors has brought us.
Even so, there are certain security risks that come with the use of DMA. In how far they are a concern depends on the application, circumstances and mitigation measures. Much like the humble
memcpy() function, DMA is a very powerful tool that can be used for great good or great evil, depending on how it is used. Even as we have to celebrate its existence, it’s worth it to consider its security impact in any new system.
Businesses face the need to store ever-larger volumes of information, across a growing number of formats.
Business data is no longer confined to structured data in orderly databases or enterprise applications. Instead, businesses may need to capture, store and work with documents, emails, images, videos, audio files and even social media posts. All contain information that has the potential to Boost decision-making.
But this presents challenges for IT systems that were designed with structured rather than unstructured data in mind.
That is because technologies that efficiently store databases, for example, are not well suited to the larger file sizes, data volumes and long-term archival needs of unstructured data.
Industry analysts IDC and Gartner estimate that about 80% of new enterprise data is now unstructured. Clearly, there is a business benefit in being able to keep and analyse that data, and in some cases long-term storage is mandated for compliance reasons.
But traditional storage technologies were not designed for either the volume or variety of such data.
As Cesar Cid de Rivera, international VP of systems engineering at supplier Commvault, points out, differing file sizes alone – say a video file versus a text document – present issues for storage. And enterprises face dealing with what he describes as “dark pools of data”, generated or moved automatically from a central system to an end-user’s device, for example.
Also, data is generated in other systems outside conventional IT, such as software-as-service (SaaS) applications, internet of things (IoT) endpoints, or even potentially from machine learning and artificial intelligence (AI). This data also needs to be found, indexed and stored.
This puts pressure on storage infrastructure. And enterprises are increasingly finding that a single approach to storage – all on-premise or all-cloud – fails to deliver the cost, flexibility and performance they need. This is leading to growing interest in hybrid solutions or even technologies, such as Snowflake, that are designed to be storage agnostic.
“The criteria to consider are the volume, the data gravity – where it is being generated, where it is being used, computed or consumed – security, bandwidth, regulations, latency, cost, change rate, transfer required and cost,” says Olivier Fraimbault, a board director at SNIA EMEA.
“The main issue I see is not so much storing massive amounts of unstructured data, but how to cope with the data management, rather than the storage management of it.”
Nonetheless, firms need to consider conventional storage performance metrics, especially I/O and latency, as well as price, resilience and security for each possible technology.
The conventional approach to storing unstructured data on-site has been through a hierarchical file system, delivered either through direct-attached storage in a server, or through dedicated network-attached storage (NAS).
Enterprises have responded to growing storage demands by moving to larger, scale-out NAS systems. The on-premise market here is well served, with suppliers Dell EMC, NetApp, Hitachi, HPE and IBM all offering large-capacity NAS technology with different combinations of cost and performance.
Generally, applications that require low latency – media streaming or, more recently, training AI systems – are well served by flash-based NAS hardware from the traditional suppliers.
But for very large datasets, and the need to ease movement between on-premise and cloud systems, suppliers are now offering local versions of object storage.
The large cloud “superscalers” even offer on-premise, object-based technology so that firms can take advantage of object’s global namespace and data protection features, with the security and performance benefits of local storage. However, as SNIA warns, these systems typically lack interoperability between suppliers.
The main benefits of on-premise storage for unstructured data are performance, security, plus compliance and control – firms know their storage architecture, and can manage it in a granular way.
The disadvantages are costs, including upfront costs, a lack of ability to scale – even scale-out NAS systems hit performance bottlenecks at very large volumes – and a lack of redundancy and, possibly, resilience.
This has led firms to look at cloud storage, for reasons of lower initial costs and its ability to scale.
For object storage – and almost all cloud storage is object-based – there is also the ability to handle large volumes of unstructured data efficiently. A global namespace and the way metadata and data are separate improves resilience.
Also, performance is moving closer to that of local storage. In fact, cloud object storage is now good enough for many business applications where I/O and especially latency are less critical.
Cloud storage cuts the (up-front) cost of hardware and allows for potentially unlimited long-term storage. Nor do firms need to build redundant systems for data protection. This can be done within the cloud provider’s services or, with the right architecture, by splitting data across multiple suppliers’ clouds.
Because data is already in the cloud, it is relatively straightforward to relink it to new systems, such as in a disaster recovery scenario, or to connect to new client applications via application programming interfaces (APIs). With Amazon’s S3 the de facto object storage technology, business applications are easier than ever to connect to cloud data stores.
And with data in the cloud, users should see little or no practical performance hits as they move around their organisation or work remotely.
Disadvantages of cloud storage include lower performance than on-premise storage, especially for I/O-heavy or latency-intolerant applications, potential management difficulties (anyone can spin up cloud storage) and potential hidden costs.
Even though the cloud is often viewed as a way to save money, hidden costs such as data egress charges can quickly erode cost savings. And, as SNIA EMEA’s Fraimbault cautions, although it is now fairly easy to move containers between clouds, this becomes harder when they have their own data attached.
As a result, a growing number of suppliers now offer hybrid technologies that can combine the advantages of local, on-premise storage with object technology and the scalability of cloud resources.
This attempt to create the best of both worlds is well suited to unstructured data because of its diverse nature, varied file sizes, and the way it might be accessed by multiple applications.
A system that can handle relatively small text files, such as emails, alongside large imaging files, and make them available to business intelligence, AI systems and human users with equal efficiency is very appealing to CIOs and data management professionals.
Also, organisations also want to future-proof their storage technologies to support developments such as containers. SNIA’s Fraimbault sees the way hybrid cloud is moving to containers, rather than virtual machines, as a key driver for storing unstructured data in object storage systems.
Hybrid cloud offers the potential to optimise storage systems according to their workloads, retaining scale-out NAS, as well as direct-attached and SAN storage, where the application and performance needs it.
But lower-performance applications can access data in the cloud, and data can move to the cloud for long-term storage and archiving. Eventually, data could move seamlessly to and from the cloud, and between cloud providers, without either the application or the end-user noticing.
Meanwhile, other suppliers, such as Microsoft, are increasing their support for hybrid storage through its Azure Data Factory data integration service.
However, the idea of truly location-neutral storage still has some way to go, not least because cloud business models rely on data transfer charges. This, the Enterprise Storage Forum warns, can lead to bloated costs.
Indeed, a accurate survey by supplier Aptum found that almost half of organisations expect to increase their use of conventional cloud storage. As yet, there is no one-size-fits-all technology for unstructured data.
Establishing the new UK regulatory regime for chemicals, following the UK’s withdrawal from the EU, looks set to take longer, and cost industry significantly more than the government had expected.
The Department for Environment, Food and Rural Affairs (Defra) is consulting on plans to extend the deadlines chemicals manufactured or imported into Great Britain to be registered with the Health and Safety Executive under UK Reach (registration, evaluation, authorisation and restriction of chemicals). Registrations include information on hazards, uses and exposures of the substance.
UK companies had already spent around £500 million to comply with EU Reach. Defra’s impact assessment suggests it will cost industry a further £1.3–3.5 billion to comply with UK version. The UK’s Chemical Industries Association (CIA) trade body had previously pegged that potential cost at around £1 billion.
Industry already paid £500 million for access to 27 markets, and now we are to effectively come up with something new, but the same, and to pay again
UK regulations after Brexit included transitional rules to allow companies to demonstrate their compliance with EU Reach, and to later provide full registration data to the UK. Deadlines of October 2023, 2025 and 2027 were set, depending on volume and hazard profile. Defra is considering extending these by up to 3 years, after concerns raised by stakeholders. Around 22,000 substances have gone through the initial UK process.
Cost estimates have risen because, although there was a good understanding of EU Reach registrations held by UK manufacturers, knowledge on imports was lacking. The rise ‘reflects the significant increase in the number of chemicals [now known] to be involved in the UK supply chains, partly because the UK is probably importing a lot of mixtures,’ explains Nishma Patel, policy director at the CIA.
The key demand from industry is ‘to get clarity, as soon as possible,’ she says. CIA members regularly ask what will be required and by when. Multinational companies in the UK will need to consider whether the additional cost to keep their products in the UK is worthwhile. ‘These aren’t short-term decisions,’ adds Patel.
Tim Doggett, chief executive of the Chemical Business Association (CBA), is concerned that if Defra sets too onerous requirements for UK Reach that ‘some chemicals will no longer be commercially viable and simply disappear off the market’. If that happens, manufacturing industries will have to source alternatives. This issue will take time to emerge. ‘It will be a sort of domino effect, as those registration decisions are made,’ warns Patel.
Initially, industry groups proposed that the UK regulators tap into publicly available information, including that submitted under the EU Reach process, and focus on evaluating chemicals that were a priority for the UK. ‘Under the current proposal, we duplicate the whole [EU Reach] process,’ says Patel.
Companies would prefer if UK regulators focused on chemicals of national priority for the UK and requested data on these, says Patel. ‘Industry would work with that,’ she adds, ‘rather than trying to access data for 22,000+ substances, some of which would never be looked for five, possibly 10 years.’ Even if companies submitted data on thousands of substances, UK authorities are unlikely to have the resources to review them rapidly.
There was disappointment that the Brexit negotiations failed to secure a data-sharing agreement on chemicals, but many commentators view this as inevitable. ‘I don’t think that was ever a real possibility, but it seemed to take the government a while to appreciate that,’ says Doggett.
Without shared access to the European Chemical Agency’s (Echa) database, the HSE must effectively duplicate that effort to create its own. A large portion of the expected UK Reach costs relate to UK companies buying access to existing data, rather than repeating tests to generate the same data. ‘Some firms operating in GB will either own data already or have trading relationships with data owners, so they are likely to be able to access data on relatively favourable terms,’ said Defra.
It is very difficult to minimise cost to industry, without leaving consumers and the environment less protected
The entire process remains uncertain. ‘The current deadline of October 2023 is highly unlikely to be met,’ says Doggett, whose members represent the entire chemical supply chain, including many small and medium enterprises, distributers and hauliers. He says industry remains frustrated at the prospect of duplicating safety tests carried out for EU Reach, which could include animal testing. About 60% of UK chemical exports go to the EU, so must follow EU rules. A firm exporting to the UK and EU must now register a product and include safety data under two separate regulatory regimes.
‘Industry already paid £500 million for access to 27 markets, and now we are to effectively come up with something new, but the same, and to pay again,’ says Doggett. ‘We need to take a pragmatic approach to all regulations. It doesn’t mean loosening regulations.’ Defra said it is now exploring an ‘alternative transitional registration model’, with the aim of reducing costs to business of transitioning to UK Reach.
Compromise brings its own concerns, however. ‘Civil servants have been handed an almost impossible task of squaring a circle within existing policy parameters,’ says Chloe Alexander, policy advisor at UK charity Chem Trust. ‘It is very difficult to minimise cost to industry, without leaving consumers and the environment less protected, within a system independent of the EU’s.’ She says it would be unconscionable to deregulate chemical safety data requirements to reduce costs to industry.
We want to maintain or Boost existing benchmarks, but to be as similar as possible is to everybody’s benefit when it comes to moving products
Divergence between the regimes could even lead to dumping onto the UK market. The EU is considering banning many per- and polyfluoroalkyl substances (PFASs), says Nigel Haigh, European environmental policy expert at Chem Trust. ‘If that happened, but didn’t happen in the UK, there would be a severe risk of surplus PFASs in the EU being dumped onto the UK market,’ he adds. PFASs are highly persistent in the environment and of increasing concern to regulators.
Chem Trust suggests emulating the Swiss approach, which does not require full registration data for chemicals that are registered in EU Reach, but instead follows EU risk management decisions by default.
This might be politically unpalatable, but it would keep the country aligned with the EU and reduce duplication for industry. At the CIA, Patel says this ‘is certainly an option,’ but adds that ‘previous conversations with government indicated that the Swiss model is not suited to the UK’. For now, industry is pursuing a compromise that will minimise cost and duplication.
‘We don’t want to diverge for the sake of divergence,’ says Doggett. ‘Ultimately we want to maintain or Boost existing benchmarks, but to be as similar as possible is to everybody’s benefit when it comes to moving products.’
Business analytics is an increasingly powerful tool for organisations, but one that is associated with steep learning curves and significant investments in infrastructure.
The idea of using data to drive better decision-making is well established. But the conventional approach – centred around reporting and analysis tools – relies on specialist applications and highly trained staff. Often, firms find they have to build teams of data scientists to gather the data and manage the tools, and to build queries.
This creates bottlenecks in the flow of information, as business units rely on specialist teams to interrogate the data, and to report back. Even though reporting tools have improved dramatically over the past decade, with a move from spreadsheets to visual dashboards, there is still too much distance between the data and the decision-makers.
Companies and organistions also face dealing with myriad data sources. A study from IDC found that close to four in five firms used more than 100 data sources and just under one-third had more than 1,000. Often, this data exists in silos.
As a result, suppliers have developed embedded analytics to bring users closer to the data and, hopefully, lead to faster and more accurate decision-making. Suppliers in the space include ThoughtSpot, Qlik and Tableau, but business intelligence (BI) and data stalwarts such as Informatica, SAS, IBM and Microsoft also have relevant capabilities.
Embedded analytics adds functionality into existing enterprise software and web applications. That way, users no longer need to swap into another application – typically a dashboard or even a BI tool itself – to look at data. Instead, analytics suppliers provide application programming interfaces (APIs) to link their tools to the host application.
Embedded analytics can be used to provide mobile and remote workers access to decision support information, and even potentially data, on the move. This goes beyond simple alerting tools: systems with embedded analytics built in allow users to see visualisations and to drill down into live data.
And the technology is even being used to provide context-aware information to consumers. Google, for example, uses analytics to present information about how busy a location or service will be, based on variables such as the time of day.
Indeed, some suppliers describe embedded analytics as a “Google for business” because it allows users to access data without technical know-how or an understanding of analytical queries.
“My definition generally is having analytics available in the system,” says Adam Mayer, technical product director at Qlik. “That’s not your dedicated kind of BI tool, but more to the point, I think it’s when you don’t realise that you’re analysing data. It’s just there.”
The trend towards embedding analytics into other applications or web services reflects the reality that there are many more people in enterprises who could benefit from the insights offered by BI than there are users of conventional BI systems.
Firms also want to Boost their return on investment in data collection and storage by giving more of the business access to the information they hold. And with the growth of machine learning and artificial intelligence (AI), some of the heavy lifting associated with querying data is being automated.
“What we are trying to do is provide non-technical users the ability to engage with data,” says Damien Brophy, VP for Europe, the Middle East and Africa (EMEA) at ThoughtSpot. “We’re bringing that consumer-like, Google-like experience to enterprise data. It is giving thousands of people access to data, as opposed to five or 10 analysts in the business who then produce content for the rest of the business.”
At one level, embedded analytics stands to replace static reports and potentially dashboards too, without the need to switch applications. That way, an HR or supply chain specialist can view and – to a degree – query data from within their HR or enterprise resource planning (ERP) system, for example.
A field service engineer could use an embedded analysis module within a maintenance application to run basic “what if” queries, to check whether it is better to replace a part now or carry out a minor repair and do a full replacement later.
Also, customer service agents are using embedded analytics to help with decision-making and to tailor offers to customers.
Embedded systems are designed to work with live data and even data streams, even where users do not need to drill down into the data. Enterprises are likely to use the same data to drive multiple analysis tools: the analytics, business development or finance teams will use their own tools to carry out complex queries, and a field service or customer service agent might need little more than a red or green traffic light on their screen.
“The basic idea is that every time your traditional reporting process finds the root cause of a business problem, you train your software, either by formal if-then-else rules or via machine learning, to alert you the next time a similar situation is about to arise,” says Duncan Jones, VP and principal analyst at Forrester.
“For instance, suppose you need to investigate suppliers that are late delivering important items. In the old approach, you would create reports about supplier performance, with on-time-delivery KPI and trends and you’d pore through it looking for poor performers.
“The new approach is to create that as a view within your home screen or dashboard, continually alerting you to the worst performers or rapidly deteriorating ones, and triggering a formal workflow for you to record the actions you’ve taken – such as to contact that supplier to find out what it is doing to fix its problems.”
This type of alerting helps businesses, because it speeds up the decision-making process by providing better access to data that the organisation already holds.
“It’s partly businesses’ need to move faster, to react more quickly to issues,” says Jones. “It’s also evolution of the technology to make embedded alert-up analytics easier to deliver.”
Embedded analytics suppliers are also taking advantage of the trend for businesses to store more of their data in the cloud, making it easier to link to multiple applications via APIs. Some are going a step further and offering analytical services too: a firm might no longer need expertise in BI, as the supplier can offer its own analytical capabilities.
Again, this could be via the cloud, but serving the results back to the users in their own application. And it could even go further by allowing different users to analyse data in their own workflow-native applications.
A “smart” medical device, such as an asthma inhaler, could provide an individual’s clinical data to their doctor, but anonymised and aggregated data to the manufacturer to allow them to plan drug manufacturing capacity better.
“Data now is changing so quickly, you really need intraday reporting,” says Lee Howells, an analytics specialist at PA Consulting. “If we can put that in on a portal and allow people to see it as it happened, or interact with it, they are then able to drill down on it.
“It’s putting that data where employees can use it and those employees can be anyone from the CEO to people on operations.”
But if the advantage of embedded analytics lies in its ability to tailor data to the users’ roles and day-to-day applications, it still relies on the fundamentals of robust BI systems.
Firms considering embedded analytics need to look at data quality, data protection and data governance.
They also need to pay attention to security and privacy: the central data warehouse or data lake might have robust security controls, but does the application connecting via an API? Client software embedding the data should have equal security levels.
And, although cleaning data is always important for effective analytics and business intelligence, it becomes all the more critical when the users are not data scientists. They need to know that they can trust the data, and if the data is imperfect or incomplete, this needs to be flagged.
A data scientist working on an analytics team will have an instinctive feel for data quality and reliability, and will understand that data need not be 100% complete to Boost decision-making. But a user in the field, or a senior manager, might not.
“Embedded analytics continues the democratisation of data, bringing data and insight directly to the business user within their natural workflow,” says Greg Hanson, VP for EMEA at Informatica.
“This fosters a culture of data-driven decision-making and can speed time to value. However, for CDOs [chief data officers] and CIOs, the crucial question must be: ‘is it accurate, is it trustworthy and can I rely on it?’ For embedded analytics programmes to be a success, organisations need confidence that the data fuelling them is from the right sources, is high quality and the lineage is understood.”
CDOs should also consider starting small and scaling up. The usefulness of real-time data will vary from workflow to workflow. Some suppliers’ APIs will integrate better with the host application than others. And users will need time to become comfortable making decisions based on the data they see, but also to develop a feel for when questions are better passed on to the analytics or data science team.
“Organisations, as part of their next step forward, have come to us with their cloud infrastructure or data lakes already in place, and they started to transform their data engineering into something that can be used,” says PA’s Howell. “Sometimes they put several small use cases in place as proof of concept and the proof of value. Some data isn’t as well used as it could be. I think that’s going to be a continually evolving capability.”
GUEST OPINION: As the network perimeter blurs and attack surfaces expand for Australian organisations, it's becoming clear that a new defensive posture and approach is required.
Practitioners are familiar with the dynamism of cybersecurity. It may be part of the reason they got into it in the first place.
Taking one measure alone, 55 common vulnerabilities and exposures (CVEs) were recorded on average every day last year, a record. 2022 is already on track to exceed that. These vulnerabilities are spread throughout the full stack of technologies used by organisations. With systems and applications as interconnected as they are today, multiple vulnerabilities can be chained together by attackers to Boost their chances at exploitation, or to escalate attacks.
Attackers also have a greater choice of potential targets and entry points to choose from, while conversely practitioners have more gates to protect and can limit traffic through their organisation.
A accurate study found 75% of Australian businesses are now living with a vastly increased attack surface. The largest contributor to this is the increased use of web applications to engage with dispersed and often 'location agnostic' employees, customers, and other stakeholders. The increased number of endpoints inevitably expands the attack surface and exposes companies to new vulnerabilities. Often companies are not aware of the status of all devices accessing their resources.
In addition, the need for infrastructure modernisation and digitalisation has led to adoption of newer technologies, further expanding the risk.
While Australian CISOs may say they have everything covered, the survey found that security maturity could well be further developed and nurtured.
But our research simultaneously shows that when you dig down and talk to people lower down in the security hierarchy, the reaction and response is inconsistent at best, and all over the place at worst.
Frontline security in the SOCs are chasing to keep up with the combined impacts of a rapidly widened attack surface, changing architectures, more people working remotely and ongoing digitalisation.
In short, current cybersecurity postures are struggling to align with dynamic attack surfaces.
That needs to change.
Breaching the moat
Cybersecurity teams have traditionally focused on preventing all attacks, using what might be referred to as a 'castle and moat' approach. The 'castle' is the office network, protected by the 'moat' (the network perimeter). Everyone inside the 'moat' was trusted, not so anyone outside it. A 'drawbridge' lowered over the 'moat' allowed traffic movements to be controlled in and out.
This works on the assumption that people work within a walled, protected environment, that they are accessing sensitive data and systems mostly from within an office on corporate-owned devices.
Most organisations don't operate like this anymore. Only 18% of Australian companies say that they still have this traditional 'castle and moat' defence.
The reason for that is that this defensive model simply does not work when the network perimeter becomes blurred. It also does not offer workable prevention against the growing dynamism of the attack surface.
Adapting to change
A completely different approach to cybersecurity is required.
The desirable end state - easier said than done - is to embrace an adaptive cybersecurity posture, supported by people, process and technology - that is more responsive to the dynamism of today's cybersecurity landscape.
As research firm Ecosystm notes, "anticipating threats before they happen and responding instantly when attacks occur is critical to modern cybersecurity postures. It is equally important to be able to rapidly adapt to changing regulations. Companies need to move towards a position where monitoring is continuous, and postures can adapt, based on risks to the business and regulatory requirements. This approach requires security controls to automatically sense, detect, react, and respond to access requests, authentication needs, and outside and inside threats, and meet regulatory requirements."
Adaptation is also likely in future to involve artificial intelligence. A golden example of applying AI adaptively for cybersecurity would be to be able to detect the presence of code, packages or dependencies that are impacted by zero-days or other vulnerabilities, and to block those threats. That may be some way off yet - it would require a model, and enough time and data to train it. But it's an example of the thinking and discussion on adaptive cybersecurity that is currently taking place.
Tackling attack surface
While an adaptive cybersecurity posture is the end game, there are things Australian organisations can do in the interim to get a better handle on their environments.
An interim goal could be to better protect web applications - the single largest contributor to an expanded attack surface in Australia.
For this, development and security teams alike should embrace security-as-code and policy-as-code. Using a security-as-code approach allows developers to communicate runtime security assumptions to the application infrastructure at deployment. Limiting the types of requests that an application has to process can be more efficient as it allows pre-processing of inputs at the edge of the application infrastructure, rather than inside the application.
In addition, next-generation web application firewalls (WAFs) provide teams more options to deal with threats. They allow security to be addressed in a more automated way, detecting and either logging or blocking malicious request traffic before it reaches the web application.
Leveraging WAFs and content delivery networks (CDNs) should be part of any holistic defence-in-depth security strategy, and offer a pathway to immediate protection, as well as towards more adaptive forms of cybersecurity protection.
Napier’s Multi-org Deployment Model Delivers Reduced Total Cost of Ownership for Regulated Organisations
Napier, provider of leading anti-financial crime compliance solutions, has announced a multi-org deployment capability for regulated firms looking to streamline and scale their financial crime risk management technology across multiple business entities.
Napier’s multi-organisation (multi-org) capability is a versatile approach to RegTech cloud deployment that offers firms the opportunity to deploy its technology across multiple geographies and business units in a single tenancy environment, while segregating and configuring the solution to best fit each business unit’s requirements.
The multi-org approach closes the last technical gap between optimal performance and the benefits of a single tenancy environment. It enables distinct risk management controls within each of a firm’s segregated business units which are aligned to their rigorous information security requirements and regulatory commitments. The provision builds upon system security as a priority, removing technological and operational risk by giving organisations full control to designate permissions, access data, and manage workflows within its business units.
This offers organisations a considerable reduction in total cost of ownership, as the capability allows firms to scale the deployment of Napier’s financial crime risk management technology with a more efficient approach, removing the requirement to implement a new single tenancy for each business unit.
Julian Dixon, founder and CEO at Napier, said: “In times where organisations are under pressure to rise to financial crime challenges against a backdrop of staggering inflation rates and rising costs, the ability to implement technology swiftly and effectively is key to operational success. We know the very real struggles that firms of all sizes are up against when it comes to balancing business and revenue objectives with meeting regulatory requirements.
“That’s why enabling them to scale capabilities across their depth and breadth without having to implement a new single tenancy for each business unit offers considerable reduction in total cost of ownership. We are thrilled to bring this capability to the market and have dedicated Napier’s best minds to developing this new capability which we know will revolutionise the way our clients approach compliance.”
Napier’s latest innovation is beneficial to all regulated financial institutions who need certainty, clarity, and efficiency for financial crime risk management across multiple business entities and want to simultaneously reap the savings and security benefits of a single tenancy environment.
View source version on businesswire.com: https://www.businesswire.com/news/home/20220621005978/en/
BOSTON – Cinchy, the dataware vendor that’s changing the way organizations work with data, today released “The Rise of Dataware: An Integration-Minimizing Approach to Data,” a comprehensive analyst report that highlights a fundamental shift taking place in the data management sector. It focuses on a distinctive architectural approach that redefines the relationship between data and applications, and essentially eliminates the need for data integration as we know it. The study, conducted by consulting and research firm Eckerson Group, differentiates dataware from popular approaches to data centralization—such as data warehouses and data lakes—and illustrates how decoupling data from software enables organizations to support the creation of “autonomous data” that delivers significant business benefits.
“A radical new architectural approach called ‘dataware’ redefines the relationship between data and applications and eliminates the need for data integration,” the report notes. “So many of the tools we use today focus on bringing data together from multiple applications, but, with dataware, the data is never separated to begin with.” With widespread acceptance “it will flip the data technology industry on its head.”
The report shows how after decades of allowing software to fracture data and then tasking data teams to put the pieces back together, the market needs a unified model of data from the beginning, rather than after the fact. Dataware does just that: It creates a shared data layer for applications that also provides a foundation for analytics. “Dataware is an evolution in both technology and methodology,” the report notes. “Just as software liberated form from function, enabling the same hardware to perform multiple tasks, dataware liberates data from code.”
“Over the years, organizations have become entirely application-centric—there’s an app for everything, and a database for every app,” said Dan DeMers, founder and CEO of Cinchy. “Dataware redraws the boundaries so that when we think of the overall data architecture, we’re able to take a fresh look at how apps manage the data, and reverse the equation. This is the best and fastest path to building a data-centric culture.”
The report highlights attributes shared by all dataware platforms:
“The concept of separating data from applications has major ramifications not only for the data world but also for software development,” the Cinchy/Eckerson report notes. It also delineates the differences between dataware and other technologies such as data warehouse, data virtualization, data lake, data lakehouse, master data management, and microservices. While each of these approaches meets particular needs within the enterprise, none is as bold as dataware in reimagining the relationship between data and applications. “Dataware has the potential to revolutionize both software development and analytics,” it states.
For users in every jurisdiction, the greatest benefit of dataware comes from the increase in control over data. When unlimited solutions can be supported from the same uncopied data, enforcing access and privacy policies becomes much easier. When governance professionals can programmatically grant access, it reduces risks associated with both data leaks and costly regulatory repercussions. This also eliminates any potential confusion over the ‘real’ values of particular pieces of data. Ultimately, an investment in dataware today carries a potential upside as the overall approach of separating data from applications gains traction in every business.
About Eckerson Group
Eckerson Group is a global research, consulting, and advisory firm that helps organizations get more value from data. Its experts think critically, write clearly, and present persuasively about data analytics. It specializes in data strategy, data architecture, self-service analytics, master data management, data governance, and data science. Organizations rely on Eckerson Group to demystify data and analytics and develop business-driven strategies that harness the power of data.
Cinchy is leading the next tech revolution to help organizations gain simplified, streamlined, and authorized access to data. Cinchy provides the world’s first comprehensive dataware platform that unlocks data from enterprise apps and connects it together in a universal data network. Developed for real-time data collaboration, Cinchy Dataware Platform addresses the root cause of data fragmentation and data silos, eliminates the cost and need for time-consuming data integration, and mitigates risks of data duplication.
With Cinchy, midsize and enterprise organizations gain agility to accelerate digital transformation, reduce the time and cost to build applications by more than 50%, decrease project delivery risks, Boost data governance, and enable effortless sharing of quality data across systems and users. The company has been named a Deloitte Technology Fast 50 Company to Watch, a "Top Pick" at TechCrunch Disrupt and a Top Growing Canadian Company by The Globe and Mail. Visit: https://cinchy.com
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