Picture this. There is a hunter who has set up his post right in the center of a dense jungle known to be densely populated with wild animals. He has a watchtower set up with equipment that alerts him whenever an animal approaches his bait, so he can prepare to aim at his target. But one day, his equipment starts malfunctioning.
He gets alerted even at the move of a tree branch. When it happens more frequently, he gets frustrated and loses energy, missing the real wild animals around his watch tower, trapped in the same jungle he came to hunt.
In the world of cybersecurity, the absence of data quality can lead to graver consequences! Let us explore why data quality is important in cybersecurity, along with some best practices to ensure data quality.
What is data quality?
With organizations across industries scaling new levels of productivity and efficiency through digital transformation, data has grown in volume and complexity. However, it has given birth to the challenge of ensuring data quality. But what is data quality?
Data quality is when data serves its purpose by being accurate, complete, reliable, and relevant.
For a resilient cybersecurity posture, data quality is a must. Why?
What are the consequences of not ensuring data quality? Why is it important?
One of the biggest challenges in cybersecurity that organizations struggle with is high alert noise. When regular activities get flagged as cyber threats, it can lead to missing critical security alerts.
Therefore, data quality is fundamental to an organization’s defense. With it, security systems can quickly and effectively detect and respond to cyber threats.
The following are the reasons why data quality is a must:
- Without accurate/complete data, detection mechanisms can’t function effectively in catching threats. It can make organizations vulnerable to cyber threats.
- It can delay incident response considerably. In high-stress situations, every little information can help stop damage and respond to threats. Any inaccuracy can lead to considerable delay in response and allow attackers the opportunity to cause further damage.
- It can cause a waste of time and energy for security teams, leading to quicker burnout and financial losses for an organization.
- It can cause tech sprawl with organizations trying to solve data accuracy problems by adding new solutions and technologies.
- The absence of data quality can attract huge penalties/fines due to non-compliance as regulatory bodies require organizations to ensure reliable and accurate reporting of data (like GDPR, HIPAA, and PCI-DSS).
- It can take the focus away from critical security problems, exposing organizations to serious threats.
What are the challenges to ensuring data quality?
The following are some challenges faced by organizations while ensuring data quality:
- Siloed solutions
- Rising complexity of threats
- Absence of data validation measures
- Human error – lack of awareness/negligence/complacency
- Limited expertise to ensure data quality
Best practices to ensure data quality
The following are some of the best practices to ensure data quality:
- Implement data validation measures that identify and remove malicious and incomplete data.
- Develop and implement governance policies that ensure that data is validated and updated in a systematic manner, with strict policies for handling data in transit and out of transit.
- Identify and implement measures to detect and respond to unauthorized access to data.
- Use advanced tools for data quality management for automated data validation and accuracy.
- Regularly assess infrastructure to preemptively identify and address data quality problems.
