Stop Chasing Your Data
There has been an exponential growth in data volume due to the growing use of SaaS, IaaS, and PaaS in the last couple of years. Data today flows from devices to on-premise systems, cloud applications to external third parties and business partners. This causes enterprises to face various technical, legal, and process challenges to secure data based on their location or infrastructure. Using the traditional perimeter security solutions and legacy systems results in gaps within their systems, exposing the organization to the risk of cybercrimes. However, the looming fear of data breaches, compliance mandates, and the changing threat vectors has made the speed of deploying security solutions extremely critical. Changing the focus to more data-centric is the only last line of defense that will guard any organization from compromised data.
Traditionally, security solutions fall prey to long deployment cycles, including policy creation and administration, end-user training, and awareness. These have been the tried-and-tested models to reduce false positives by constantly tweaking and updating the policies. Although the intention is to ensure long-term business continuity, the duration of the deployment cycle increases the risk of bad actors stealing sensitive data. Hence, the damage would already be done when the IT team has deployed the security solution.
It is crucial for security solution providers to focus on a time-to-value approach. Applying modern automated deployment techniques requiring little or no IT administration can remove the need for user participation, policy creation, and management overheads. These deployment techniques leveraged the visibility around data created by other data-focused solutions like DLP, Secure gateways, CASBs, etc.
Let’s take a deeper look into why automation is a crucial aspect of data-centric security.
Why Automation Reason #1: Consistent and Accurate Data-centric Security
It is practically impossible to get all the users to define the sensitivity of every data asset. Even if that works out, it is essential to remember that there is still significant dependence on user discretion. It could lead to users incorrectly assigning data security policies. The IDC’s Data Protection and Privacy Survey of 2021 revealed that many organizations applied stringent security policies to data that didn’t require it. However, they didn’t use any security policies for sensitive data like Intellectual Property, Contracts, Research data, etc.
While manual data protection is error-prone and liable to produce false positives, automation removes, or at least reduces, the possibility of any such errors. Removing the user from the context and answering “What to protect” through automation using inputs from other data-focused solutions brings consistency to the system. This ensures that the data adheres to the enterprise’s security policies. As a result, there is minimal scope for human error.
Why Automation Reason #2: No Impact on User Productivity
By setting policies and rules based on the data trends of the organization, it is possible to automate the discovery, classification, and data security processes for both incoming and outgoing data. As a result, the IT team doesn’t need to monitor the data constantly, nor does it require end-user participation, thus preserving their productivity.
Well-implemented automation processes do not hinder user productivity but enhance it. Automating the security policies to the data flowing from one device to another immunizes the data, allowing it to be visible even as it travels outside the organization. This makes the data suitable for external collaboration, boosting user productivity even as workplaces shift from office to home and even enter a hybrid model.
Why Automation Reason #3: Less Administrative and IT Overheads
Earlier, managing an organization’s security policies was a manual process, which required complete administrative intervention. Not only was it an arduous process for the administration department, but it also affected the operations of the rest of the organization due to the lag in access protection requirements. As a result, overly permissive policies would be implemented, defeating the very purpose of data-centric security.
Automation helps an organization determine the right policies to be applied to data at a large scale as it gets fetched from storage media such as SAP, SharePoint, Salesforce, etc. Additionally, automation helps an organization cope with the ever-changing privacy requirements to ensure compliance and ease of policy updates.
Why Automation Reason #4: Scalability
Automation allows an organization to efficiently address business dynamics, e.g., onboarding new joiners, departing employees, transfers, and external collaboration. Eliminating the overheads of defining security policies beforehand for each business case and automatically federating them at runtime in real-time for storage and collaboration allows an enterprise to implement and adopt the solution at scale. This, in a true sense, makes the security seamless transparent to the end-users.
Using automation with artificial intelligence makes it possible to automate the platforms to audit the process with prebuilt analytics. Using Digital Rights Management (DRM) solutions with auditing capabilities enables active data usage tracking. This gives the security teams the awareness of sensitive data use and a better context of unauthorized activities to make well-informed policy decisions.
When it comes to managing elaborate data privacy systems, it has been commonly observed that the users are the weakest links. It is crucial to reduce user intervention and provide an unbiased monitoring system for efficient data security. Taking the user out of context and protecting the data at source automatically via integration with other security tools, mail and messaging systems, or storage and collaboration systems will allow enterprises to successfully implement and adapt their data security initiatives.
In a nutshell, automating the data-centric security processes has the following advantages:
- Ability to easily track data and instantly spot any malpractices.
- No toll on productivity as any manual intervention happens only in an untoward event.
- Achieve scalability and broader user adoption due to lesser overheads around end-user awareness and training.