Unlock innovation through integrating data security and data governance
Two trends progressing in parallel are transforming the IT landscape across all industries: journey to cloud and artificial intelligence (AI) and machine learning (ML).
In one trend, to gain agility, reduce costs, and realize competitive advantages, enterprises are modernizing their IT ecosystem by moving more of their infrastructure to the cloud. In a second trend, to analyze their data, unlock valuable insights, and fuel innovation, organizations are employing AI and ML applications. Intersecting these two trends is the adoption of cloud-native data platforms that collect and catalog data from across the enterprise.
This exciting digital transformation means that organizations will have the tools they need to enable access to data to more of their employees, while also increasing the scope and depth of analysis through AI and ML. With platforms like IBM Cloud Pak® for Data, data scientists can accelerate innovation through increasing — by orders of magnitude — the volume of analysis they are able to perform.
But new tech advances can create a host of privacy, security and compliance concerns. Rapid innovation can sometimes outpace security, introducing new risk. Thwarting those issues requires you to:
- Maintain visibility across containers and applications
- Determine if infrastructure vulnerabilities place data at risk
- Isolate compromised containers, databases and applications during a breach
- Determine if standard security measures of the host platform will meet your needs
- Ensure the right people have access to the right data
- Discern whether data is being used in compliance with privacy regulations
These concerns are only part of a comprehensive security strategy designed to eliminate vulnerabilities.
Addressing regulatory compliance requirements can be costly and time-consuming under any circumstance. In adopting a cloud-native data and AI platform, migrated to a new environment, your organization will confront the same regulatory challenges. To make this transformation to a democratized data successful, you will need to improve your organization’s security governance, security and compliance practices.
Many enterprises store data across disparate locations, creating silos of storage, controls and visibility. Governed data planes — like those offered by Azure, AWS, Google or IBM Cloud Pak for Data — provide strong governance features, but they are not security solutions.
Additional security challenges can arise in the data stores themselves, including on-premises and hybrid multicloud databases connected to the data plane, and with containerized databases — some of them open source — stored on the Cloud Pak for Data.
To exert more granular control over their data, security leaders may want to take power into their own hands to ensure their data is protected. For example, an enterprise may opt to spin up containerized databases like Mongo DB, PostgreSQL or IBM’s Db2, on the platform. This practice provides greater flexibility to scale data storage capacity and performance easily, but can bring security tradeoffs. Those databases may store critical data assets — such as financial, legal documents, trade secrets, or research and development — as well as regulated data—like customers’ personally identifiable information.
Given the sensitivity of this data stored on or accessed by data and AI platforms, business leaders should consider:
- Who owns and manages the platform?
- Are there platform-based vulnerabilities?
- If you are deploying container images from third parties, what vulnerabilities exist?
- Can you enforce separation of duties from cloud vendors or administrators with respect to privileged data?
- In a Trust but Verify world, can you ensure that you have complete audit history for data usage?
Understand that these considerations cover only databases stored on the cloud-native platform and do not address external data stores linked to the platform. For these environments, you will also need to achieve visibility, access control, and be able to detect and contain potential threats. Beyond these security concerns, there remain the ever-present compliance challenges.
As more social and economic activity shifts online, companies continue to gather more and more personal information about their customers. With each new high-profile data breach in the headlines, there is increasing pressure on governments to regulate and for organizations to improve their protection efforts. To satisfy regulatory audits, you must be in compliance with regulations in force where you do business.
Auditing and reporting on regulatory compliance can be a slow and resource intensive process. Complications can multiply when an enterprise’s data footprint expands. To avoid compliance roadblocking innovation, organizations must deploy solutions that simplify and accelerate the compliance reporting process through automation.
How to secure a modernized cloud-native data landscape
With many organizations storing data stored across the enterprise, data governance and security leaders must protect their company’s data assets across all data sources. Organizations are adopting data and AI platforms that provide strong data governance and prepare data for AI applications. Given the value and sensitivity of data assets on these platforms, data and security leaders would be well-advised to also deploy enterprise-grade data security and compliance reporting solutions to protect all data environments uniformly.
The right solution can help secure, control, and monitor access to your data, from blocking or quarantining risky or suspicious users to scanning the entire environment for vulnerabilities. And an effective solution can simplify the auditing and reporting process. Uniting a best-of-breed data security solution with a modern, governed data analytics and AI platform can help organizations accelerate the pace of innovation safely and securely.
Interested in how to bring best-in-class security to your data and AI platforms? Try our Guardium Data Protection demo.