Establishing Information Lifecycle Management for Utilities

The $2.7 million fine recently levied on a large West Coast utility for security lapses is a stark reminder of the consequences of inadequate data governance.¹ While US utilities may not be directly subject to laws like the California Privacy Rights Act (CPRA) or General Data Protection Regulation (GDPR), the reality is clear: weak data management exposes companies to financial, operational, and reputational harm.

By establishing an ILM strategy built on clear policies and standards, rigorous oversight and strong governance, utilities can improve compliance, prevent data misuse, and safeguard critical assets. Beyond regulatory requirements, proactive data management drives operational efficiency, accelerates decision-making, and reinforces long-term resilience in an increasingly data—driven industry.

A robust information lifecycle management (ILM) strategy is critical for utilities to mitigate risks, ensure data integrity, and enhance security. By establishing an ILM framework grounded in clear policies, internal standards, and strong governance, utilities can improve compliance, prevent data misuse, and safeguard critical assets. In addition to meeting regulatory obligations, proactive data management drives operational efficiency, supports timely decision—making, and promotes long—term resilience in a data—centric environment.

Key Stages When Implementing an ILM Framework

An initial data governance program can be set up through the establishment of an ILM framework, which is essential to prevent data leaks and security breaches at every stage of the data lifecycle. These stages include creating, storing, maintaining, using, distributing, archiving, and disposing. Implementation considerations for each stage are set out below and outlined in Figure 1.

• Define: Identify areas where data is located, group the data into certain types, provision notices for appropriate use of information.
  
  
• Create: Establish data classification (public, internal, confidential, restricted), tagging, and privacy impact assessments. Define a single source of truth to eliminate inconsistencies and ensure data accuracy.
  
  
• Store (data at rest): Secure sensitive data with encryption, anonymization, and tokenization, reducing the risk of unauthorized access.
  
  
• Maintain (data in motion): Protect data transfers with advanced encryption standard (AES) 256, simple authentication and layer security (SASL) authentication, and data transfer project (DTP) to prevent leaks during transmission.

• Use: Strengthen access controls with role—based access control (RBAC), audit logs, and data—centric audit and protection (DCAP) to ensure only authorized users interact with critical data.
  
  
• Distribute: Prevent unauthorized exposure by masking sensitive data, encrypting external transfers, and implementing data loss prevention (DLP) measures.
  
  
• Archive: Optimize storage with access recertification, tiered storage strategies, and live data controls to ensure secure, cost-effective data retention.
  
  
• Dispose: Reduce risks by enforcing data retention policies, ensuring secure deletion protocols, and defining device destruction standards to eliminate outdated or unnecessary data.

By embedding security and governance at every stage, an ILM framework not only protects critical infrastructure but also enhances data-driven decision-making, operational efficiency, and regulatory compliance. Now more than ever, organizations must take proactive steps to secure their most valuable asset—data.

Establishing a Data Governance Hierarchy

The implementation of an ILM framework will only work if there is leadership support from the top and an understanding of the datasets at the bottom of the organization. It requires all teams to work together with a shared objective to ensure data is trustworthy and reliable.

As such, a structure of two leadership governance committees is recommended, along with data stewards and data subject matter experts (SMEs) from each type of data domain and support from a data management department. An example structure is shown in Figure 2.

Latest Trends in ILM Framework Automation

Organizations are increasingly investing in the automation of controls across the following areas to bring sustainable control and efficiency benefits to the ILM framework.

Discovery. Organizations should invest in capabilities that enable effective discovery of sensitive data across the enterprise. This includes deploying tools tailored for location-based discovery, configuring in—house DLP technologies, and integrating discovery into core platforms. A successful discovery effort should encompass both structured and unstructured data across various environments.

Classification. Building upon existing technologies, utilities can enhance data governance by applying classification to unstructured data repositories in addition to structured sources. This includes environments such as data lakes, collaboration platforms, file servers, and email systems. Applying consistent classification standards supports better data handling and access control.

Anonymization. To support privacy requirements, organizations should implement additional capabilities that complement discovery tools—specifically to de-identify or anonymize personally identifiable information (PII). These capabilities help ensure that sensitive data can be used for analytical or operational purposes without compromising individual privacy.

Privacy Impact Assessment. Establishing dedicated workflows for privacy impact assessments (PIAs) is essential. These workflows promote business accountability and ensure privacy risks are evaluated and mitigated before engaging in activities that involve PII. Integrating PIAs into business processes helps institutionalize privacy as a core consideration in decision-making.

Disposal, Retention, and Archival. Utilities should define and implement policies that govern the retention, deletion, and archival of information. This includes embedding lifecycle rules directly within business applications and systems to automate data disposal once retention requirements have been met or to archive data to alternative storage environments for long-term preservation.

Third-Party Engagement. Ensuring consistent governance across vendor and third-party interactions is a key component of information lifecycle management. Establishing standardized workflows for vendor engagement strengthens oversight, mitigates risks, and promotes alignment with internal data policies and compliance requirements.

Conclusion

Establishing a strong information  lifecycle management (ILM) framework is essential for utilities navigating today’s intensive data environment. A well-defined ILM approach addresses internal and external data dependencies, while ensuring every stage—from data creation to disposal—embeds governance, privacy and security.

By embracing standardized workflows, a hierarchical governance model, and the latest ILM innovations, utilities can unlock operational efficiency, strengthen data security, and future-proof their information management strategy. 

Capco can help you navigate this transformation. With deep industry expertise and a focus on innovation, we partner with utilities to design and implement scalable ILM frameworks that deliver long-term value. 

References

¹ https://www.power-eng.com/business/policy-and-regulation/pg-e-fined-2-7m-by-feds-for-third-party-s-data-breach/