The growing need for data protection

  • 5 minutes

Contoso Ltd., a global financial services company, sees an increasing volume of sensitive data spread across its cloud services, including employee files, financial records, and AI-generated content from tools like Microsoft 365 Copilot. While these technologies support faster decisions and flexible work environments, they also create security risks. Sensitive data moves across cloud platforms, remote endpoints, AI applications, and non-Microsoft services. As data volumes increase, so do the challenges of managing and protecting it.

Access controls help, but protecting sensitive data depends on knowing where it's stored, who can access it, and how it's used.

The consequences of data breaches and insider threats

When organizations fail to secure sensitive data, the consequences can be severe. Breaches can result from external attacks, insider threats, or accidental data leaks. No matter the cause, organizations face financial loss, regulatory penalties, reputational damage, and operational disruptions.

Security agencies continue to report the growing scale of these threats. According to ENISA's 2024 Threat Landscape report, data-related threats have surged, affecting public administration (12%), digital infrastructure (10%), finance (9%), and business services (8%). Data compromise incidents rose in 2023 and 2024, reinforcing the need for strong data protection measures.

The Cybersecurity and Infrastructure Security Agency (CISA) reports in its Insider Threats 101 fact sheet that the average cost of an insider risk incident reached $16.2 million per organization in 2023, with an average of 86 days to identify and contain these incidents. Insider threats can result from accidental exposure, compromised credentials, or malicious intent, making proactive data protection essential.

Organizations must account for risks like:

  • Data breaches from unauthorized access: Attackers exploit weak access controls, compromised credentials, or unsecured data storage to steal sensitive information. Enforcing strong authentication, least privilege access, and encryption helps reduce exposure.
  • Social engineering attacks: Threat actors use phishing, business email compromise, or other manipulation techniques to trick employees into exposing sensitive data. Employee training, email security controls, and verification processes help prevent these attacks.
  • Data leaks and misconfigurations: Improperly secured cloud storage, accidental sharing, and access misconfigurations expose data unintentionally. Security audits, automated access controls, and clear data governance policies reduce the likelihood of exposure.

Without a structured security approach, these risks lead to widespread data exposure and long-term business challenges.

Risks organizations face

Organizations need to protect sensitive data from both external and internal threats while staying compliant with regulatory requirements. Key risks include:

  • External threats: Cyberattacks, phishing, and malicious activities that target sensitive data for financial gain or espionage.
  • Insider risks: Employees or contractors who accidentally or intentionally expose data.
  • Compliance challenges: Complex and evolving regulatory requirements that demand consistent data governance and reporting.
  • AI security risks: AI tools that access or process sensitive data can introduce risks if not properly controlled.

The need for a proactive approach

Reactive security measures are often too late to prevent damage. Organizations need a proactive data protection strategy that includes:

  • Data classification and labeling: Identify and mark sensitive data to apply consistent security policies.
  • Data loss prevention (DLP) and retention policies: Control data sharing, prevent leaks, and meet compliance requirements.
  • Insider risk management tools: Detect risky behavior and investigate security incidents before data is compromised.
  • Dynamic security controls: Apply protections based on real-time risk signals to adjust security enforcement as user risk changes.
  • AI security measures: Control how sensitive data is used or processed in AI models to prevent exposure.

By taking a preventive approach, organizations protect sensitive information, maintain compliance, and reduce the financial and operational consequences of security incidents.