Data Classification Take 30: Why A One-Size-Fits-All Solution Will Never Work - Seguridad Mania.com - España y América Latina
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Martes 15 de Jun, 2021
In a world where data loss is costing organisations millions of dollars in fines, there is not one organisation who shouldn’t have data protection at the top of their agenda – especially in the current climate with workers spread more remotely than ever. The trouble with the security software landscape today is that too many solution providers are promising “one-size-fits-all” tools; something that is simply too good to be true. The lack of functionality allowing bespoke tailoring leaves you unable to meet your specific needs. Many of the tools in a “one-size-fits-all” platform are essential, but packaged as they are for the standard organisation they often don’t meet your needs. We know that any security control implemented in an organisation imposes operating constraints on the business, so the tools need to work in the exact way you need not the way that someone thinks you should work to be worth the investment. For example, badly deployed DLP, or overly aggressive post-delivery controls, can make the organisation lose faith in the solution and make you roll back even the sensible controls you have in place. When it comes to data protection and security within your organisation, the solution you choose is going to have multiple touchpoints with business processes and productivity tools. Therefore, when looking at building your security ecosystem, you are going to want a wide range of complementary security and data management solutions to avoid having a serious mess to resolve at some point. Join us to understand: • The challenges with “one-size-fits-all” solutions • Why your technology ecosystem needs to be broad • The importance of agile solutions • Getting the best ROI from your technology investments • Why data classification is critical for business success Register now! Boldon James and Titus comprise best-of-breed data classification solutions at HelpSystems.
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