Combatting Data Security Cluelessness

Written by Nikhil Girdhar, Sr. Director of Data Security, Securiti AI.

In cybersecurity, the old adage you 'can’t protect what you can’t see' rings especially true. While the initial step of discovering and classifying sensitive data is critical, it's just the beginning. Many security teams find themselves at a crossroads, clueless how to effectively transform data classification insights into robust data security controls.

Understanding Data Context Through The Lens of Security Controls

Comprehensive data context is crucial for effectively addressing various aspects of data security, ranging from misconfiguration vulnerabilities and access controls to privacy and compliance regulations. In assessing and implementing data security controls, the focus extends beyond simply identifying sensitive data; it involves a nuanced understanding of how diverse metadata attributes interrelate. These include data asset type, location, data stewards, people subjects, applicable laws, users, network and Identity and Access Management (IAM) configurations, encryption status, residency, threat activity, and more.

However, gathering the full context surrounding sensitive data is challenging. Information about these attributes is often scattered across various security, privacy, governance, and compliance tools and teams. This fragmentation hinders security teams' ability to meet data security and compliance obligations, enable business users to leverage data effectively, and support other IT teams such as Chief Privacy Officer (CPO) and Chief Data Officer (CDO) offices in their duties.

Addressing this challenge, requires an understanding of sensitive data context from the lens of security controls and sharing the insights across multiple teams. Let’s look at a few examples.

Implementing Data Compliance Policies

Compliance policies vary based on data type, industry, geographical constraints, and business needs. For instance, an e-commerce company might search for Payment Card Industry (PCI) data in non-PCI compliant locations, such as test and development environments, to identify and address compliance violations.

Enforcing Least-Privileged Access Controls

It's essential to identify paths through which users and roles inherit permissions to access files and tables containing sensitive data. This helps understand who can access what data within a company. Analyzing these pathways against a user’s actual use of sensitive data provides insights for rightsizing permissions based on the principle of least-privilege data access.

Reducing Misconfiguration Vulnerabilities

With numerous pending security patches and misconfigurations, managing vulnerabilities in data systems is daunting. Prioritizing the remediation of vulnerabilities in environments and data systems containing sensitive information can optimize resource allocation within vulnerability management and developer teams.

Adopting Artificial Intelligence (AI)

With the rapid adoption of traditional AI models and Large-Language Models (LLMs), AI governance becomes critical. Security teams need to understand which data assets connected to AI models through data pipelines contain sensitive data. This is crucial for compliance with AI regulations and preventing data exposure while enabling safe AI adoption.

Data Sovereignty Controls

Laws such as the General Data Protection Regulation (GDPR) impose strict controls on cross-border data transfers. Understanding if data users in a particular location are accessing personal information of European data subjects in violation of the regional laws can inform the establishment of corporate binding rules between a company’s regional business entities.

Breach Risk Analysis

Under Security and Exchange Commission (SEC) rules, organizations must disclose any material security breaches. Assessing the materiality of a breach involves estimating the type of personal data and the number of unique identities compromised in affected data systems. Associating penalty information is also necessary to quantify financial risk.

Operationalizing Data Security Controls with a Data Command Center

Empowering teams with a unified layer of data context is crucial for combating data security challenges. By establishing connections between sensitive data and its metadata attributes, a comprehensive data intelligence layer equips security teams with the insights necessary for effective implementation of comprehensive data security controls.

This same data intelligence layer can extend beyond the realm of data security teams, benefiting other parts of the organization, including data privacy, governance, and compliance. For instance, the data privacy team may require the enforcement of encryption on personal data due to specific privacy regulations. Similarly, the data governance team might need the implementation of policy-based access controls to share sensitive data with a select group of users for a project. By fostering alignment and collaboration across multiple teams, a central data intelligence layer can serve as the foundation for operationalizing a cross-functional data command center. This command center is instrumental in enabling the safe use of data for various projects, including AI innovation, while ensuring compliance and security controls.