You have to first question, was the sensitive data in the warehouse even necessary to perform to analytics, most likely not. PII isn’t necessary for analytic use cases. De-identify your data before putting it into analytic systems. If “reverse etl” might be necessary downstream, then tokenize the PII and store the PII in a vault. Most of these vulnerabilities are related to your data culture and the tradition or a belief that hanging onto PII is necessary for the mission. Start with your data culture.

This has to be taken into 2 steps: 1- Damage Control 2- Long term prevention Immediate Action or Damage Control: Isolate: Contain the breach. Investigate: Determine the root cause. Remediate: Fix the vulnerability. Notify: Inform affected parties. Learn: Conduct a post-mortem. Long-Term Prevention: Strong Access Controls: Limit access, enforce strong passwords, and use multi-factor authentication. Data Protection: Encrypt sensitive data and implement data loss prevention measures. Regular Security Audits: Conduct regular assessments to identify vulnerabilities. Employee Training: Educate employees on security best practices. Cloud Security: Leverage cloud-native security features and best practices.

To protect our data, we use a thorough, layered approach. Regular audits and continuous monitoring help us detect unusual activity early on. Role-based access control limits access to sensitive information strictly to roles that need it, reducing unnecessary exposure. Our systems are automatically updated and patched to stay secure, while ongoing training educates our team on best security practices, like recognizing phishing threats. Data encryption in storage and transit, paired with a robust backup protocol, ensures data protection and availability. Lastly, a detailed incident response and recovery plan enables us to manage breaches quickly, minimizing damage and recovery time, and reinforcing future security.

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To prevent future data breaches if a team member exposes sensitive data in a shared datawarehouse following measures can be taken: 1.implement RBAC to limit who can view or edit sensitive data. 2.Regularly review & update access permissions to ensure they align with current roles. 3.Encrypt sensitive data at rest & in transit to protect it from unauthorized acess. 4.Motitoring & Auditing 5.Training & awareness for maintaining data privacy security best practices & foster culture of awareness. 6. Incidence Response plan. 7. Data Governance policies 8. Regular assesments 9. Collaboration with IT security 10. Use of Data Masking or Tokenizaataion.

A detecção rápida de violações é essencial, mas a prevenção é ainda mais eficaz. Além das medidas técnicas é fundamental contar com um plano de resposta a incidentes bem definido e testado regularmente. A organização deve ser ágil para adaptar suas medidas de segurança às novas ameaças e tecnologias

Data need to be labelled with varying rules of classification based on their access rules The access rules should be realistic and reflect the true sensitivity of the data The truly PII data will be redacted and will be exposed on need to know basis Data warehousing does not need PII the data will be segmented and the segments will be analysed for the trends Companies tend to store excessive data in the warehouse and needlessly expose them to danger Only the segments able to expose insight should be available Keeping mounds of data will slow down the platform, expose the sensitive data, will not deliver insights, and difficult to maintain Data warehousing should be about insights and not building huge stack of data

Some tips: Immediate Response: Restrict access to exposed data, notify relevant teams, and conduct a root cause analysis to understand the breach. Strengthen Access Controls: Implement role-based access, data masking, and conduct regular access reviews. Data Security Policies: Establish clear data handling policies and classify data by sensitivity. Security Training: Provide regular training on data security practices and handling sensitive information. Monitoring and Alerts: Set up real-time alerts for unusual access and monitor access logs. Data Governance: Appoint data stewards for oversight and regular compliance reviews. Regular Security Audits: Conduct audits and vulnerability assessments to keep security measures robust.

One thing that’s always helpful, in addition to IDS/IPS and other countermeasures, is implementing stored procedure based alert imbedded into the DBMS itself encompassing all tables so that you get an alert if your tables are altered. Also, in case of a live-leak (hacker still in your DB system copying your DB) is knowing that the Delayed Response doesn’t help much and take action immediately to stop the bleeding instead (Immediate Response). IMHO, the Delayed Response to identify and catch the hacker is almost impossible. So, why spend crucial time instead of implementing countermeasures immediately?