The Evolution of Data Protection
Data has penetrated every facet of our lives. It has evolved from an imperative procedural function into an intrinsic component of modern society. This transformative eminence has introduced an expectation of responsibility on data processors, data subjects and data controllers who have to respect the inherent values of data protection law. As privacy rights continually evolve, regulators are faced with the challenge of identifying how best to protect data in the future. While data protection and privacy are closely interconnected, there are distinct differences between the two. To sum it up, while data protection is about securing data from unauthorized access, data privacy is about authorized access – who defines it and who has it. Essentially, data protection is a technical issue whereas data privacy is a legal one. For industries that are required to meet compliance standards, there are indispensable legal implications associated with privacy laws. And guaranteeing data protection may not comply with every stipulated compliance standard.
Data protection law has undergone its own evolution. Instituted in the 1960s and 70s in response to the rising use of computing, re-enlivened in the 90s to handle the trade of personal information, data protection is becoming more complex. In the present age, the relative influence and importance of information privacy to cultural utility can’t be understated. New challenges are constantly emerging in the form of new business models, technologies, services and systems that increasingly rely on ‘Big Data’, analytics, AI and profiling. The environments and spaces we occupy and pass through generate and collect data.
Technology enthusiasts have been adopting new data management techniques such as ETL (Extract, Transform, and Load). ETL is a data warehousing process that uses batch processing and helps business users analyze data which is relevant to their business objectives. There are many ETL tools which manage large volumes of data from multiple data sources, manage migration between multiple databases and easily load data to and from data-marts and data warehouses. ETL tools can also be used to convert (transform) large databases from one format or type to another.
The Limitations of Traditional DLP
Quaint DLP solutions offer little value. Most traditional DLP implementations mainly consist of network appliances designed for primarily looking at gateway egress and ingress points. The cooperate network has evolved; the perimeter has pretty much been dissolved leaving network-only solutions that are full of gaps. Couple that with the dawn of the cloud and the reality that most threats emanate at the endpoint and you understand why traditional, network- appliance only DLP is limited in its effectiveness.
DLP solutions are useful for identifying properly defined content but usually falls short when an administrator is trying to identify other sensitive data, such as intellectual property that might contain schematics, formulas or
The data protection criterion has to transform to include a focus on understanding threats irrespective of their source. Demand for data protection within the enterprise is rising as is the variation of threats taxing today’s IT security admins. This transformation demands advanced analytics and enhanced visibility to conclusively identify what the threat is and deliver the versatile controls to appropriately respond, based on business processes and risk tolerance.
Factors Driving the Evolution of Data Protection
Current data protection frameworks have their limitations and new regulatory policies may have to be developed to address emerging data-intensive systems. Protecting privacy in this modern era is crucial to good and effective democratic governance. Some of the factors driving this shift in attitude include;
Regulatory Compliance: Organizations are subject to obligatory compliance standards obtruded by governments. These standards typically specify how businesses should secure Personally Identifiable Information (PII), and other sensitive information.
Intellectual Property: Modern enterprises typically have intangible assets, trade secrets, or other propriety information like business strategies, customer lists, and so on. Losing this type of data can be acutely damaging. DLP solutions should be capable of identifying and safeguarding exigent information assets.
Data visibility: In order to secure sensitive data, organizations must first be aware it exists, where it exists, who is utilizing it and for what purposes.
Data Protection in The Modern Enterprise
As technology continues to evolve and IoT devices become more and more prevalent, several new privacy regulations are being ratified to protect us. In the modern enterprise, you need to keep your data protected, you have to be compliant, you have to constantly be worried about a myriad of like malicious attacks, accidental data leakage, BYOD and much more. Data protection has become essential to the success of the enterprise. Privacy by Design or incorporating data privacy and protection into every IT initiative and project has become the norm.
The potential risks to sensitive corporate data can be as tenuous as the malfunction of small sectors on a disk drive or as broad as the failure of an entire data center. When contriving data protection as part of an IT project, there are multiple considerations an organization has to deal with, beyond selecting which backup and recovery solution they will use. It’s not enough to ‘just’ protect your data – you also have to choose the best way to secure it. The best way to accomplish this in a modern enterprise is to find a solution that delivers intelligent, person-centric and dynamic data-centric fine-grained data protection in an economical and rapidly recoverable way.