For modern enterprises, data represents an invaluable asset empowering gains in innovation, efficiency, decision-making, and competitive advantage. But maximising the potential of data while still adequately controlling access to protect privacy poses complex challenges. With stringent regulations like the EU’s General Data Protection Regulation (GDPR) now shaping the landscape, proactively balancing privacy and productivity has become essential.
The Data Dilemma
Today’s heavily digital business environment means companies across virtually every industry rely extensively on data to optimise countless aspects of their operations and strategy. Data provides unparalleled insights that can be leveraged to enhance predictive analytics, personalise customer experiences, target marketing outreach, streamline production and supply chains, and guide strategic decisions. It is a critical driver of productivity and performance.
However, data also carries inherent risks if not managed diligently. From external cyber-attacks to insider threats, unintended exposure and non-compliant usage, data can clearly introduce substantial vulnerabilities. As data generation and collection continues growing exponentially across enterprises, keeping up with evolving threats and leaks can quickly become an overwhelming challenge. Stringent regulations like GDPR also impose strict legal standards and expectancies around data protection, with hefty financial penalties for non-compliance.
For most companies today, fully locking down and restricting access to data is not a feasible answer, even though it may reduce risks. Excessive controls and limitations would severely inhibit the ability to leverage data for wider productivity, efficiency, and innovation gains across operations. But on the other hand, leaving sensitive datasets insufficiently protected and monitored also poses unacceptable risks from a compliance, reputational and competitive standpoint. Organisations clearly face a dilemma in allowing productive usage of data while still adequately safeguarding it.
Implementing Graded Data Protection
A more nuanced risk management approach to address this dilemma involves establishing graduated tiers of data protection aligned to relative sensitivity levels. Setting blanket maximum restrictions on all data regardless of its actual risk profile and use cases inhibits legitimate access needed for productivity. But leaving all data lightly protected does not account for datasets warranting tighter control.
A three-tiered data protection model provides a scalable and dynamic framework:
- Top tier data with maximum sensitivity, like customer financial information, intellectual property, or other regulated datasets, requires the strongest protections such as role-based access controls, end-to-end encryption, multi-factor authentication, limited dissemination, and strict access logs. Despite stringent controls, we can selectively leverage this data in aggregated and anonymised form to avoid exposing raw sensitive attributes.
- Medium tier data like operational reports, product designs, and internal communications warrants moderate protections including managed access control lists, network-level defences like firewalls, and system monitoring. This data can typically be readily accessed by most authorised internal users under the existing security controls.
- Low tier data like general company announcements, product marketing materials, and other unregulated public information requires only basic protections like anti-malware defences and backups. Broad internal dissemination and access to such data can be allowed to foster collaboration with minimal risk.
This targeted, context-based strategy ensures the most critical sensitive datasets stay rigorously protected without categorically limiting access to the lower-risk information needed for productivity.
Automating Data Governance
Managing appropriately tiered data protection and enabling broad legitimate usage at scale requires implementing proper IT governance of the full data lifecycle. But manual governance approaches quickly become inefficient, inconsistent, prone to oversights, and unable to keep pace as data generation and collection grows nearly exponentially. Automating governance processes is essential for viability as datasets proliferate.
Intelligent data catalogue platforms provide centralised visibility into what data exists, where it resides across systems, who is accessing it, and how it flows across the enterprise. Automated classification processes can tag and categorise data by relative sensitivity levels based on criteria like regulated status, personal identifiers, confidentiality, and business impact if compromised. Role-based usage guidelines and limitations can then be programmatically enforced at the point of access based on user identity. Redundancy checking further compares data against pre-defined policy requirements and flags potential gaps or compliance violations.
Cultivating Shared Data Responsibility
Technical controls and governance policies alone will fail without an accompanying culture of responsibility and stewardship among employees at all levels who interact with data. Even with automated governance and controls in place, people represent the last line of defence when it comes to properly securing data while enabling its usage. Ongoing security and privacy awareness programs are critical to empowering staff as data stewards.
Proper understanding of policies allows staff to play an active role in identifying and escalating potential vulnerabilities, mis-configured controls, risky practices, and other issues for remediation before they become threats. Awareness initiatives also help frame privacy controls and governance not as obstacles to innovation but rather as enablers to tap into data securely.
Outsourcing the DPO Function
Designing and overseeing progressive data protection programs that balance access and security requires specialised expertise around privacy, cyber risk, and compliance. But qualified chief data protection officers (DPOs) are both scarce and expensive investments. One emerging option is outsourcing DPO requirements to qualified third-party privacy advisory services.
Outsourced DPOs can provide invaluable guidance and oversight in creating pragmatic data governance frameworks tailored to an organisation’s culture, operational realities, and risk tolerance levels. They advise business leaders on safely optimising data usage while ensuring compliance.
Conclusion
Modern digitally driven businesses rely heavily on data to gain competitive advantages. But they must also diligently protect it through balanced controls that allow productivity without excessive risk. With appropriately tiered and thoughtful measures in place, data can enable innovation and steer strategy rather than stifle productivity due to blanket restrictions.
