In today’s data-driven economy, information is both a valuable asset and a significant risk. Enterprises handle vast amounts of sensitive information, from personally identifiable information (PII) and protected health information (PHI) to financial records and proprietary business insights. As artificial intelligence (AI) becomes increasingly integrated into business operations, safeguarding sensitive data has never been more critical. 

Organisations now face a new challenge: how to handle sensitive information or records effectively in an era where AI is reshaping business operations.  

What qualifies as sensitive information? 

Sensitive information refers to data that, if misused or disclosed without authorisation, could result in financial loss, regulatory violations, reputational harm, or breaches of privacy. In 2024, the global average cost of a data breach was $4.88 million.  

While the definition can vary across industries and regulatory frameworks, common categories of sensitive information include: 

  • Personally Identifiable Information (PII): Data points that can be used to identify individuals, including names, addresses, phone numbers, and social security numbers. 
  • Protected Health Information (PHI): Medical records, insurance details, and patient histories protected under healthcare privacy regulations such as HIPAA. 
  • Financial Information: Bank account details, credit card numbers, transaction records, and payroll information. 
  • Trade Secrets and Intellectual Property: Proprietary data that provides a competitive advantage, including business strategies, internal research, and patents. 
  • Regulated Data: Information governed by specific laws, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA), which mandates strict handling and disclosure requirements. 

Identifying sensitive information is the first step toward ensuring its protection, but effective governance demands a comprehensive strategy that extends beyond identification. 

The risks of mishandling sensitive information 

The consequences of failing to secure sensitive information are substantial, often resulting in financial penalties, reputational damage, and operational disruptions. The primary risks include: 

  • Data breaches: Unauthorised access to sensitive or restricted information can lead to identity theft, financial fraud, and corporate espionage. 
  • Regulatory non-compliance: Organisations that fail to comply with regulations such as GDPR or CCPA sensitive information requirements may face severe fines and legal consequences. 
  • Operational inefficiencies: Disorganised or poorly managed data can slow down workflows, hinder decision-making, and increase storage costs. 
  • Increased exposure to cyber threats: Retaining unnecessary data expands the potential attack surface, increasing the likelihood of cyber incidents. 

Mitigating these risks requires a proactive, structured approach to data governance, particularly in environments where AI plays a significant role. 

Best practices for automating sensitive data protection with AI 

AI and machine learning technologies are increasingly being used to automate the identification and protection of sensitive information: 

  1. Automate data discovery and classification 

Manually identifying sensitive information across vast datasets is no longer practical for large organisations. AI-powered platforms like EncompaaS automatically discover, classify, and tag sensitive information across structured and unstructured data repositories. 

  1.  Implement role-based access controls (RBAC) 

Effective control of access to sensitive or restricted information ensures that only authorised individuals can view or handle specific data. Role-based access controls help reduce the risk of accidental exposure or malicious misuse. 

  1. Encrypt and anonymise sensitive data 

Encrypting sensitive data both at rest and in transit ensures protection from unauthorised access. For datasets containing CCPA sensitive information, anonymisation techniques help maintain compliance while still allowing for data analysis. 

  1. Continuously monitor data usage 

Ongoing monitoring allows organisations to track how sensitive data is accessed, processed, and shared. AI-driven data preparation platforms like EncompaaS monitor key assets in critical systems with customisable rules. This mitigates accidental misuse and over-retention, detects unusual patterns and enables quick responses to potential data breaches. 

  1. Automate data retention and deletion policies 

Automated policies can help organisations manage redundant, obsolete, or trivial (ROT) data, reducing compliance risks and operational inefficiencies. By disposing of unnecessary data in a defensible manner, organisations can maintain compliance with regulatory requirements and minimise storage costs. 

How EncompaaS supports sensitive information protection 

The EncompaaS platform provides enterprises with the capabilities necessary to manage sensitive information securely by: 

  • Automatically discovering and classifying data across repositiories to reflect the sensitivity of the information 
  • Applying governance policies aligned with organisation-specific requirements and international data protection regulations 
  • Continuously monitoring data quality and usage to ensure compliance and proactively mitigate risks 

This allows organisations to address both current and emerging data security challenges, ensuring that sensitive information remains protected in an increasingly complex regulatory environment. 

Organisations across industries have successfully leveraged EncompaaS to streamline data governance, reduce risk, and improve operational efficiency: 

Large-scale automations and cost-cutting solutions 

A global professional services firm used EncompaaS to gain visibility into over 500 million documents, automating the discovery, classification and management of sensitive data.  

By identifying and removing over 10 million redundant items, the firm reduced compliance risks, strengthened data security and optimised operational efficiency.  

EncompaaS enabled proactive governance, ensuring accurate, high-quality data while cutting storage costs and enhancing decision-making. 

A 30% reduction in data over-retention 

In another success story, a global insurer partnered with EncompaaS to tackle rising storage costs, compliance risks and inefficiencies caused by redundant and over-retained data.  

By automating data discovery, classification and disposal, the insurer achieved a 30% reduction in data over-retention, saved 400 hours per employee annually, and significantly cut migration costs.  

EncompaaS enabled smarter information governance, improving data quality, security and decision-making while unlocking new business value. 

Safeguard sensitive information in the AI era 

As data volumes continue to grow and AI technologies evolve, enterprises must adopt comprehensive strategies to identify, classify, and protect sensitive information. Failure to do so can lead to severe financial, operational, and reputational consequences. 

Organisations seeking to improve their data governance posture can rely on EncompaaS to protect sensitive information with AI-driven solutions. By normalising enterprise data so that compliance and privacy obligations can be address centrally, automatically, and at scale, EncompaaS enables organisations to reduce risk, meet regulatory requirements, and maintain data integrity in a rapidly changing digital landscape.