Five ways to protect sensitive data in your organization
Build a better strategy for data governance, risk and compliance
Organizations collect a tremendous amount of data from a variety of sources, and any of these data sources could potentially contain sensitive data. Data is often relocated for warehousing, reporting, analytics, storage, testing, and application use. Data or AI models could potentially be copied multiple times over, resulting in misuse of sensitive data.
Emergence of newer technology platforms such as cloud and data lakes can exacerbate the issue further. Organizations often feel a natural tension between data governance, regulatory needs, and innovation, when a well-governed, secure environment can actually spur innovation and lead to an increase in organizational productivity. In order to understand the amount of sensitive data living across the organization and mitigate associated risks, it is important to examine the entire data landscape to ensure all regulatory requirements towards its lifecycle and correct usage are met.
The data lifecycle should be managed from creation to disposal, and everything in between. To tackle robust data challenges, most organizations have integrated 40+ tools and solutions in their portfolio, according to Gartner. Vendor consolidation is what 80% of organizations are pursuing today as an avenue for reduced costs and better security.
As organizations look to consolidate their information architecture stack, there are five considerations that are critical to your foundation for trusted data:
- Continuous auditing
- Data governance
- Data discovery
- Timely response and assessment
- Deployment anywhere
There is a growing need to create visibility across sources to manage related data. Greater availability of data, facilitated by real-time integrations, makes it possible for compliance experts to monitor a wide variety of sources. A governance, risk and compliance (GRC) solution helps to provide aggregate insights into systemic issues in controls, processes and compliance for dynamic areas such as cyber risk and data protection. This requires systems to have a connected library of risk and compliance items that can be viewed across different business dimensions.
Typically, with a data catalog at the core, organizations need to be able to create and automate policies for enterprise-wide categorizing and classification of data. This needs to occur everywhere data resides in order to ensure that the appropriate data protection measures are applied and triggered when data classified as sensitive is accessed, used, or transferred. Additional capabilities like data masking, user-based access controls for discovery, and risk assessment of unstructured data are also critical to implement for a robust approach to data governance.
As more departments within the organization express the need to manage and access data, information leaders need to focus on streamlining data operations with increased efficiency, data quality, findability and governing rules in order to provide an efficient, self-service data pipeline to the right people at the right time from any source.
Timely response and assessment
To implement changes to governance artifacts quickly, organizations need to be able to automate the reporting of personally identifiable information (PII) in order to improve accuracy and reduce audit times. Data citizens should have a holistic, real-time view of how private data is being used throughout the organization, from applications to AI models.
To meet the demands of today and stay competitive tomorrow, information architectures need to be efficient and agile. Given the dynamic nature of AI, chief data and privacy officers are looking to build collaborative workflows and automate their AI lifecycles across an array of contributors. The solution? An agile and resilient cloud-native platform that enables data citizens to succeed with AI irrespective of their unique data and cloud landscape. Container-based platforms, such as RedHat OpenShift, help realize these benefits anywhere through containerized services, container management and orchestration that can lower IT infrastructure and development costs by up to 38% per application. Plus, they can be deployed across any environment — whether on-premises or on the cloud.
IBM’s new universal data privacy framework empowers more risk-aware decisions and processes by unifying how policies are managed across disparate, ever-changing localities and data sources. Automate and simplify data governance, compliance and security practices with IBM Watson Knowledge Catalog, IBM OpenPages with Watson and the new AutoPrivacy for IBM Cloud Pak for Data.