Data breaches and cyber threats are becoming increasingly common in this digital era, and protecting valuable information is the top priority for data-driven organizations. To curb the constant issues of data being compromised, lost, and misused, a Data Protection Officer (DPO) and their teams can implement a data loss prevention (DLP) strategy and tools that will continuously monitor and analyze data to identify potential violations of security policies and stop them from evolving.
In this article, we will take a closer look at the seven steps of DLP strategies and tools that will help in enhancing the security of your IT structures.
Seven-Step Framework in Deploying DLP Strategy
If any business is handling sensitive data and operating in a regulated environment or suffers from repeated cybersecurity threats, it’s time that needs to add DLP strategies.
Proofpoint’s 2024 data loss landscape report indicates that 84.7% of enterprises have encountered data loss, with an average of 15 incidents per organization per year. This implies the importance of appropriately implementing DLP strategies.
Therefore, without any further ado, let’s understand the seven-step strategic framework of DLP:
Identify and Classify Your Data
To protect data effectively, DPOs need to know the exact type of data that they need to work on.
With the help of data discovery tools such as Informatica, Spectral, and Osano, data discovery administrators will scan the data repositories and report on findings, providing visibility of what needs to be protected. These tools further use regular expressions for their searches; they are very flexible but can be complicated to fine-tune.
After implementing data discovery, data administrators can use data classification software such as Varonis, Fortra Digital Guardian, and Imperva which will help them control users’ data access and avoid storing sensitive data in any unsure locations, reducing the risk of data leaks and data loss.
Use Data Encryption
In the data-centric world, encryption provides a two-step security measure that involves converting data into code that is only deciphered with a decryption key.
Organizations that deal with extremely sensitive forms of data are required to follow data security standards and regulations, including the Payment Card Industry Data Security Standard (PCI DSS) and the General Data Protection Regulation (GDPR). If an organization fails to comply with encrypting sensitive data, it can result in regulatory non-compliance and can lead to costly data breaches and legal penalties.
Therefore, to safeguard, data professionals can use different data encryption tools such as IBM Security Guardium Encryption, Thales CipherTrust, and Sophos SafeGuard Encryption, which add complex mathematical algorithms to data and transform it into a random series of characters that are indecipherable without the suitable decryption key.
To Know More, Read Full Article @ https://ai-techpark.com/data-l...-techniques-of-2024/
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