Do vulnerability management measures have a data problem?

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Do vulnerability management measures have a data problem

CISOs say that while security teams have a sufficient amount of data, they lack the necessary context to improve remediation results

Security leaders point out that vulnerability management departments have a plethora of data available to them for analyzing and processing. The procedures take the same amount of time as remediation efforts. This is mostly because each of the tools used for correcting the liabilities provides only a small part of the data required to solve the liability. Most security personnel focus to double down on cloud IT.

Vulnerability management teams are under constant pressure to simplify and scale the remediation procedures. These will be a challenge if information data across many tools is being manually parsed, and in silos. Clearly, security and remediation teams required better data in terms of quality, not quantity.

The issue with data

CIOs acknowledge that vulnerability management tools collate basic data like the number of liabilities detected, impacted assets or the technical severity. It allows security personnel to monitor only the most critical remedial elements of the remediation campaign. These tools hardly provide the scale of correlated detail required to ensure better remediation results.

More experienced and mature teams will leverage business intelligence tools (BI) or spreadsheets to monitor metrics like the number of previous liabilities that have been resolved, unresolved ones, and the number of newly detected liabilities since the previous scan.

While it is helpful, this data lacks context and doesn’t provide a holistic view of the remediation program. Granular data is critical in improving remediation results.

The data that is required

CISOs believe that security teams require data that allows them to prioritize remediation based on business risk and information that can guide and drive the process improvement. Data should enable them to identify weak spots and retarget remediation measures for the most vulnerable technology impacting the most critical business segments.

Another important consideration is the varying criticality of impacted tech depending on the business cycle of the organization. Teams require improved data that facilitates decision making fueled by business expectations in real-time.

Read More: Taking a Data-Centric Approach to Zero Trust to Protect Critical Assets

Additionally, remediation personnel require an understanding of how a particular resolution can impact operations. The vulnerability management solutions monitor the average time to remediation and do the vital task of fixing data liability, with the required effort, time, etc. be recorded for further analysis and knowledge base.

Such data is invaluable for CISOs. Historical data informs them which platforms require more time for patching than others and the reason for excessive time. This helps them detect process inefficiencies, personal issues, product fallibility, and the best way to manage them.

The complexity in fixing the issue

Security leaders believe that the biggest obstacle related to the improvement of vulnerability remediation is that the data is present in silos in various systems- business context data in the configuration management database, asset repository, liability data in the scanner, etc.

In addition to this, security teams may implement various vulnerability management tools that are siloed across different departments. This includes teams that scan for liabilities, IT operation technicians, threat intelligence teams, etc.

The bigger factor in the hurdle is the fact that several data points aren’t saved or stored by existing tools and solutions. Even if they do, the data is rarely sent back to the vulnerability management program.

CISOs say that efficient collection, parsing, and data analysis are vital to mature vulnerability remediation solutions. Regardless of whether spreadsheets or BI tools are used, the remediation teams must effectively decide which metrics are needed to monitor and decide reasonable KPIs. Execution against data-driven goals helps enterprises remain on track.