SLOs FOR REGULATORY DATA COMPLIANCE
Data compliance, specifically GDPR/CPRA, is a comprehensive data privacy law that applies to organizations that collect, store, or hold personal data. The current method of ensuring compliance is manual and cumbersome.
- First step would be to implement the rules in the form of some automation script or a similar tool.
- Next, manually clean and convert the data into a suitable format that could be ingested by the automation.
Proceed to process each record according to the Compliance standard’s rules. The majority of this process is done manually. Integrating compliance standards into real-time data is a complex and laborious task. Data compliance is governed by regulatory standards that must be followed for all types of data in a company, such as Data at Rest, Data In Motion, and Data in Use. All data types need to meet these compliance standards. Defining these compliance-oriented SLOs, following them during data collection or usage, and ensuring overall data compliance throughout the company is a regulatory necessity to prevent penalties.
The AIMSLO approach to data compliance focuses on maintaining regulatory compliance and following organizational governance policies. Regulations such as GDPR or CPRA are ingested to parse the language and comprehend the standards. Subsequently, models can use the data standard reasoning to suggest and take actions to keep the data compliant. These models can also learn automated practices that align with GDPR regulations, establishing them as the organizational data governance standard. With AIMSLO, the complete GDPR standard is ingested as text through digital documents, instant messaging, or email. The language, structure, and semantics are stored as a Knowledge Graph database, acting as the ultimate reference for all data compliance matters within the organization. All customer and supplier data collected is saved in the AIMSLO Knowledge Graph as an instance of the structure. This means the Knowledge Graph understands the structure and semantics of data relationships. Additionally, it allows a seamless query of individual instance data allowing AIMSLO to collectively describe an SLO to make all data within the company compliant per the GDPR standard.
For instance, the AIMSLO for GDPR compliance shows:
- Data at Rest: 100% compliant
- Data in Motion: 99.9999% compliant
- Data in Use: 99.999% compliant
By monitoring data compliance, AIMSLO achieves GDPR compliance by encrypting, anonymizing, and applying access rules for data at rest on cloud or on-premises storage devices. Data in motion involves information flow through email, instant messaging, or collaborative tools, with a minimal budget for GDPR non-compliance. Data in use, actively processed or accessed, has a larger budget for potential violations due to user access requirements.
Categorizing data types (data at rest, data in motion, data in use) and implementing real-time access rules and security measures enhance compliance and audit traceability. Targeted compliance SLO automation, such as AIMSLO, streamlines data compliance efficiently.