Here’s the article in markdown format:
Data Management and Risk Assessment: Key Takeaways
This article summarizes key points from a conference or training session on data management and risk assessment.
Data Breaches and Risks
Data breaches can occur due to various reasons, including:
- Transfer errors: Accidental disclosure of sensitive information during transfer.
- Data damage due to network failures: Data corruption or loss due to hardware or software issues.
- Human errors: Mistakes made by individuals while recording, storing, or handling data.
- Malicious acts: Intentional actions by hackers, malware, or other malicious entities.
Predictive Prioritization
The Predictive Prioritization model evaluates 150 different aspects of vulnerability across seven categories:
Categories
- Past threat patterns: Historical data on threats and their impact.
- Past threat sources: Sources of previous threats, including individuals, groups, or organizations.
- Vulnerability metrics: Quantitative measures of vulnerability, such as severity and likelihood.
- Vulnerability metadata: Additional information about vulnerabilities, like exploitability and remediation costs.
- Past hostility: Historical data on the level of hostility demonstrated by attackers.
- Affected vendor: The vendor or supplier affected by a vulnerability.
- Exploit availability using threat intelligence data: The likelihood of an exploit being used based on threat intelligence.
Threat Model Development
Predictive Prioritization uses natural language processing and machine-learning models to gather data from multiple sources and produce a threat score.
Regulatory Requirements for Transparency
To promote transparency in data risk management:
- Determine accountability and ownership: Clearly define who is responsible for data-related assets.
- Align business, data, and technology architecture: Ensure that different systems work together seamlessly.
- Preserve the value of data: Implement data preservation practices to maintain its value over time.
- Assess risks associated with creating value from data: Consider privacy, security, and regulatory compliance when leveraging data.
Improving Data Quality
To ensure high-quality data:
- Focus on accuracy, completeness, consistency, timeliness, and accessibility.
- Eliminate silos: Make data accessible to all users and break down data silos.
- Use the right data: Only use relevant and reliable data for decision-making.
- Secure your data (and metadata): Protect sensitive information from unauthorized access or breaches.
- Treat data quality as a process: Continuously monitor and improve data quality over time.