Best Practices & Tools for Data-Centric Audit and Protection (DCAP)

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What is DCAP

As the expenses,  risks, and complexity of overseeing critical data are winding wild,  organizations are being compelled to reconsider their data loss prevention strategies and embrace the idea of steady data security as a core focus.

Related to improved visibility, Data-Centric Audit and Protection (DCAP) guarantees to provide a unified level for implementing and supporting data loss prevention methods and policies across all data sources.

Data Loss Prevention: Building An Effective DCAP

DCAP is based upon a collection of products that find and classify delicate data, monitor the usage of different data sets, and set data loss prevention policy for controlling user access. Organizations have already invested heavily in these data loss prevention tools.

Numerous implementations have yielded limited value and cost significantly more than expected, since every data loss prevention product uses specific strategies and terminology, manages a specific data silo, and requires a large investment in specialization and skills.

Projecting these difficulties across the rapidly growing scope of tools, data sources, and cloud platforms feature an urgent need for proficient and stronger data loss prevention.

Organizations that have not created data loss prevention policies to coordinate security controls and management procedures across data silos need to act.

The exponential development in data use and generation is rendering current techniques of data loss prevention out of date, requiring critical changes in both solution approaches and architecture.

Organizations lack coordination of data loss prevention management and policies over their data silos, resulting in conflicting data policy authorization and usage. Data cannot be constrained inside storage silos but is constantly transposed by business processes across different unstructured and structured silos in clouds or on-premises. Merchants recognized as DCAP suppliers will create capabilities across big data platforms and adjacent data silos, either by means of partnerships or organically.

Data Loss Prevention: Market Analysis of DCAP

A few sellers offer separate data loss prevention tools using tokenization, encryption, or data masking, while others don’t offer any tools and will require the purchase of separate merchant products. In either case, these data loss prevention products won’t be integrated into a single management console and will require cautious coordination with data security policies.

The selection of these data loss prevention tools requires careful evaluation of the dangers and threats that each can offer. Since the data isn’t ensured when stored at rest, it may still be available by system administrators. Tokenizing or encrypting fields can protect the data components in use and at rest, but care must be taken that this does not affect the operation of applications.

Data Loss Prevention: Market Definition of DCAP

A DCAP product is characterized by the capacity to centrally manage data loss prevention policies across unstructured, semistructured, and structured silos or repositories. The approach will encompass security controls, for example, the capacity to control access by centrally managing privileges and classify sensitive data, activity monitoring, and data protection. Data loss prevention techniques such as tokenization, encryption, and masking can be used specifically to improve the segregation of duties against both highly privileged users and application users. The capacity to offer reporting and auditing can also support different compliance necessities.

Data discovery and classification are also core necessities for content-aware data loss prevention tools. Data loss prevention is centered on ensuring data in motion through the prevention of data leakage outside of the organization. DCAP is also focused on using data discovery and classification to help with the activity monitoring and data loss prevention of data at rest and in use in the organization.

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