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MIMECAST UNVEILS EMAIL SECURITY 3.0 STRATEGY
New Technology capabilities and integrations strengthen Mimecast’s cloud-based platform
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Mimecast has added new capabilities to its cloud-based platform comprised of integrated service components that organizations’ need to combat the latest cybersecurity challenges. These capabilities further enable customers to embrace a new approach to defending email. Mimecast’s Email Security 3.0 strategy helps organizations address threats at three distinct zones: the email perimeter, inside the organization or network and beyond the perimeter. Mimecast has incorporated new major platform innovations helping to increase efficacy with technology integrations and product capabilities, including web browser isolation, security awareness training integrations and brand exploitation protection solutions.
“The email threat landscape has changed and requires organizations to evolve from a perimeter-based discipline to a more pervasive one. For instance, tactics, like impersonation attempts, are becoming increasingly more difficult to identify as they’re happening both in email and ‘in the wild’, but organizations still need to cover off on their basic protection needs at the same time. This requires organizations to consider a new strategy when it comes to defending email,” said Peter Bauer, chief executive officer at Mimecast. “As our customers’ needs evolve, Mimecast is committed to continuing to developnew innovations into our platform to help them build a stronger cybersecurity and resilience posture.”
New enhancements to Mimecast’s cloud-based platform include:
Zone 1 – At the Email Perimeter: Now Includes Browser Isolation and Custom Security Block for Added Protection
Mimecast Browser Isolation for Email is designed to provide organizations an additional layer of protection from new phishing sites, appearing as recently as the last few hours. Mimecast Browser Isolation is engineered to mitigate the risk of emails containing links to spoofed sites by preventing a direct connection between the user’s browser on his or her device and the target web page. Users are now further protected from any potentially malicious action like the challenge of credential harvesting or malicious downloads as the browsing session is executed and contained within the Mimecast cloud rather than on the user’s device.
Peter Bauer Chief Executive Officer, Mimecast
The service also is built to provide a safe environment for security analysts and messaging administrators to investigate incidents, helping reduce time to respond, contain and remediate threats.
Zone 2 – Inside the Perimeter: Security Awareness Training is Integrated into the Mimecast Platform to Reduce Costs and Complexity
Recent research reveals that 98 percent of organizations deploy security awareness training to their employees, yet 71 percent of organizations have been hit by an attack where malicious activity was spread from one user to another.* To help organizations more effectively manage security awareness training, Mimecast Awareness Training is now fully integrated into Mimecast’s cloud-based platform. This integration is designed to enable customers to more easily administer awareness training into their Mimecast and broader security ecosystem. Customers can now reduce the cost and complexity by having all their Mimecast solutions fully-integrated, making it easy for organizations to deploy, manage and maintain their security awareness training investments from a single administration console.
Zone 3 – Beyond the Email Perimeter: Brand and Domain Protection with Machine Learning Advances Phishing Protection from Known and Unknown Attacks
Mimecast Brand Exploit Protect is engineered to deliver an innovative solution covering more than 99 percent of phishing use cases across the web. Using machine learning, it is designed to run targeted scans that identify even unknown attack patterns, blocking compromised assets before they become live attacks at the earliest preparation stages.
UNLOCKING THE POWER OF DATA
IT teams across the globe are actively looking for solutions for the challenges in creating a big data platform. As the pace of businesses continues to increase, the power of big data and its analytics continues to grow.
—By Arya Devi
Data is a strategic asset for Businesses that needs to be secured at all times and which in parts can be used for extracting good insights that will help in mapping future strategies of the Business. With the explosive growth of Big Data, the high velocity and volume of data coming in has become a challenge to manage. This is where Big data analytics comes in.
It goes without saying that Big Data is a technology that should be handled with care in terms of analysis and systematic extraction of relevant data. In the absence of the necessary care, you could end up having to deal with data sets that are too large or complex to be dealt with using data processors. Instead of determining and analyzing whether the business could use or access the data, many organizations initially focus too much of their Big Data efforts on gathering the data. All data produced is not important to the company which is to be analyzed in the initial stage itself.
“When trying to operationalize big data, many companies must first start by answering the simple question of whether the data is even worthwhile to the business. Assuming the data is useful, then additional tools may be needed to refined or transform that data so it is usable. And even more tools may be needed to give business users access to this data. In all, this underlines how important it is to focus on the business needs before collecting data,” says Adam Mayer, Senior Manager Technical Product Marketing for Qlik.
This proves that having data is not what counts. The organization has to have a vision of the kind of outcomes expected from the big data. Data sets grow rapidly, to a certain extent because they are increasingly gathered. The world’s technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s. “The company has to understand what exactly they are trying to achieve. They should have
Adam Mayer Senior Manager Technical Product Marketing, Qlik
a strategy on what they wish to drive out of their data. The data should be classified which need to be put in the business development plan. It could be financial data, sales data or the services.” says Krishan Kant Srivastava, IT Head Infrastructure Services, Landmark Hospitality.
Data classification is the first priority a business needs to understand in terms of big data. Srivastava further adds “Big data doesn’t mean, we get the data and germ it and receive the desired result out of it. Before we get the final output, in the backend, remodeling, restructuring, cleansing and date range of the data should be done in a professional way.” For this, company needs operationalization capabilities that can consistently sift through the large volume of data and find what is relevant. It is the talent of finding a signal within the noise. It helps in delivering actionable information to businesses to drive better outcomes. Big data analytics has helped healthcare improve by providing prescriptive analytics and personalized medicine, clinical risk intervention and predictive analytics, waste and care variability reduction, automated external and internal reporting of patient data, standardized medical terms and patient registries and fragmented point solutions.
Most of the organizations have plenty of data and analytics tools but they fall short when it comes to converting analytics results into action. Since,
Illyas Kooliyankal Chief Information Security Officer (at a prominent Abu Dhabi Bank)
Krishan Kant Srivastava IT Head Infrastructure Services, Landmark Hospitallity
organizations are moving deeper into advanced analytics with big data. This is because it is now understood that there are leverageable business insights to be discovered in big data. For optimum usage of the Big Data, companies should hence have a plan before the implementation.
Even with knowledge of the importance of operationalizing big data, there are various issues faced by companies. “Considering the complexities associated and the resources needed, big data operationalization needs extensive experience, and buyin. The technologies involved and the required expertise are difficult to find and at the same time, it may be costly to maintain.
In addition to this, the integration with the organizational data sources, and other proprietary technologies may be a challenging task too. " Overall the initial implementation cost may be comparatively higher although the long term business value and the intangible benefits it brings are known,” says Illyas Kooliyankal, Chief Information Security Officer at a prominent Abu Dhabi Bank.
He further adds that it is important to make sure the design and architecture of the solutions need to be comprehensively thought through to make the solution robust to provide the required business benefits and at the same time secured enough. “Information security aspects are extremely critical for the big data environment, due to consolidation of most of the valuable data that will be centrally stored in the data lake. This will be accessed by different stakeholders for various purposes, from technologists to business end users. Any security failure could be disastrous for the organization, and at the same time, for hackers and bad actors this will be the key place to target for any kind of compromises. Thorough assessments to make sure all areas of controls, covering technical, procedural and people has to be ensured by the organization to make sure the environment is rightly protected.”
Before deciding the business requirements from Big Data analytics, a testing environment has to be created. This helps to find and close the gaps.
“A testing environment has to be made where you can run a small query or automation to see whether the results can be driven out of it,” says Srivastava. He further adds that it is important to do all the processes
in a test/development environment and develop the concept in a smaller scale. “The testing environment plays a vital role. It shows the roadblocks, what needs to be aligned, how to give a better output and desired result in terms of cleansing, extracting better data or RPA,” he adds.
Across verticals business applications are used as part of daily workflows and operational processes and there is a need for real time intelligence which is made possible by embedding analytics. Business users turn to analytics to make effective decisions, which can be made even faster, as business happens, by analyzing and visualizing data right in their applications. Mayer elaborates, “Organizations are turning to embedded analytics strategies that can help them to achieve goals like boosting their competitive advantage, increasing BI adoption to more users across the organization and improving customer experience, maybe even creating new ways of doing business,” he added.
Future trends for embedded analytics indicate continuing expansion of analytics being made available outside of the organization in many verticals through the likes of customer portals and integration. As an example, retail stores could start to give more access to shopping data and provide further granular details for customers and partners alike to make better informed decisions. This could help to make great strides in tackling environmental impacts such as improving logistics from source to table, reducing food wastage and minimizing reliance on single use plastics.
In summary, each company or business will have a different scenario for Big Data. For some firms, hundreds of gigabytes of data trigger the need to consider data management options, while others it maybe a few terabytes. For either of these, operationalization of big data is the best method to manage and make optimum use of data.
Big Data Analytics sees rapid growth
The Global Big Data Analytics Market was valued at US$ 37.34 billion in 2018 and expected to reach US$ 105.08 billion by 2027 at a CAGR of 12.3% throughout the forecast period from 2019 to 2027, according to a report from Research and Markets. Increasing volume of data and adoption of big data tools will likely spur revenue growth during the forecast period.
The rapidly increasing volume and complexity of data are due to growing mobile data traffic, cloud-computing traffic and burgeoning development and adoption of technologies including IoT and AI, which is driving the growth of big data analytics market. Over 2.5 quintillion bytes of data generated every day. Data is created by every click, swipe, share, search, and stream, proliferating the demand for big data analytics market globally.
According to the survey, the number of firms investing in big data and AI more than US$ 50 million rose from 27% in 2018 to 33.9% in 2019. The global spending on big data analytics is more than US$ 180 billion in 2019 globally. Thus bolstering the big data analytics market growth.
By 2020, 90% of business professionals and enterprise analytics say data and analytics are key to their organization’s digital transformation initiatives. According to a recent research study, approximately, 58% of organizations worldwide plan to adopt big data technology in 2018. The organizations will adopt hybrid IT infrastructure management capabilities. The growing adoption of big data and AI in industries including IT & Telecom, BFSI, and Healthcare among others is further fueling the demand for the big data analytics market.
Key Market Movements
• Globally, the big data analytics market is growing at a CAGR of 12.3% for the period from 2019 to 2027.
• Large enterprises segment dominates the big data analytics market with a share of more than 60%. Owing to the increasing adoption and inclination to invest in big data technology among others.
• In addition, the SME segment expected to grow at a remarkable pace during the forecast period. This can be associated with the increasing trend of digitalization and the adoption of big data technology among others.
• North America region leads the big data analytics market and accounts for a share of more than 35% of the total revenue. The region will sustain its lead during the forecast years. This is due to the region being one of the early adopter of technological advancements.
• Asia-Pacific region anticipated registering highest growth. Asia-Pacific region expected to grow at a CAGR of more than 15% throughout the forecast period. Owing to the increasing adoption of advanced technology in countries such as China, Japan, and India among others.
