Data is one of the most valuable assets for any organization today. As cyber threats continue to increase in frequency and impact, organizations must take a proactive approach to securing their data. Here is how I create a proactive data-centric security strategy:
Understand Your Data Landscape
The first step is gaining visibility into my data landscape. I take stock of:
- What types of data I have (PII, financial, intellectual property etc.)
- Where it is stored (on-prem servers, cloud apps, endpoints etc.)
- Who has access to it
- How it moves (is it shared via email, cloud apps etc.)
This exercise allows me to understand my risk exposure and where I need to focus my efforts.
Classify Your Data
Next, I classify my data based on sensitivity and business impact. This allows me to define security policies and controls based on classification levels. For example, highly confidential data may need stringent access controls and encryption. Basic marketing collateral can have more relaxed policies.
I recommend using a simple classification scheme, such as:
- Public – Data with minimal to no security risks
- Internal – Data that provides no competitive advantage and limited business impact if exposed
- Confidential – Sensitive data that may not be shared externally
- Highly Confidential – Data with high business impact and regulatory requirements
Map Data Flows
With data classified, I map how data flows throughout my systems and business processes. This includes:
- How data is created, stored and used
- Who accesses it and why
- How it moves within systems and applications
- How it is shared internally and externally
Documenting data flows allows me to identify high-risk areas such as unnecessary data proliferation, overexposed data and gaps in security controls.
Define Data-Centric Controls
Armed with an understanding of my data landscape, classifications and flows, I can define security controls focused on protecting data itself. These include:
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Access controls – Allow only authorized users and systems access to data based on classification levels. Use role-based access controls and multi-factor authentication for sensitive data.
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Encryption – Encrypt data both at rest and in transit, especially for confidential data. Carefully manage encryption keys.
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Data loss prevention – Block unauthorized attempts to exfiltrate data across endpoints and networks. Monitor access to detect suspicious activity.
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Rights management – Control usage of data by embedding access policies into files themselves. Revoke access automatically after certain conditions.
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Data minimization – Delete unnecessary data, limit proliferation across systems, and anonymize where possible. This reduces exposure.
Plan Incident Response
Despite best efforts, data breaches can still occur. I define incident response plans tailored to different data breach scenarios. The goal is rapid containment and recovery by:
- Having an incident response team ready with defined roles and responsibilities
- Implementing robust data backup and recovery processes
- Creating a data breach notification plan for contacting customers, regulators etc.
- Testing and rehearsing response through breach simulation exercises
Monitor, Audit and Evolve
With a data-centric security program in place, I continuously monitor access patterns, user behavior and other metrics to detect risks proactively. Regular audits help identify any gaps. I also stay up-to-date on the threat landscape and evolve my strategy accordingly.
The key is to take a structured, proactive approach focused on protecting my data. By understanding my data landscape, classifying sensitivities, controlling access and being prepared to respond, I can build a resilient data-centric security strategy.
