When assessing cybersecurity risks and analyzing the data they have, IT teams are likely to make mistakes. It is inevitable, and it is human nature.
Long hours managing systems that secure businesses, night shifts, and non-stop alerts can all be common causes of errors.
How can you help understaffed and overworked cybersecurity teams?
To help overwhelmed teams, companies use data risk analysis. What exactly is that and how can it improve the security posture of companies?
Let’s find out.
What is data risk analysis?
Data Risk Analysis (DRA) refers to the analysis of data to compare the behavior of people and computer systems. The results are displayed in the report that is generated to display all high-risk threats that need to be managed by IT teams.
DRA reduces the number of security risks and detects unwanted activity within your systems early.
Today it is automatic and uses a combination of artificial intelligence and machine learning to conclude whether the system is at risk of potential incidents.
How does data risk analysis improve cybersecurity?
The purpose of data analysis is to show whether there is evidence of an already compromised part of the system.
Some of the ways it can strengthen security are:
- Get a bird’s eye view of potential risks
- Enabling early discovery of threats and flaws
- Generate reports on the state of security and possible solutions
- Highlight high-risk issues
Increased risk visibility
In data security, visibility of potential risks refers to knowing who has access to sensitive data. Also, the information must be protected and companies must know where to find it in your system at all times.
The overview shows whether the information is likely being exposed to a data breach† It also sheds light on the flaws in your system that can lead to network attacks.
Threats discovered in time
Early detection of threats is essential for businesses. The longer the threat actor is in a system, the more access it has to sensitive data.
The cost of a cyber attack increases by the minute. Unpatched errors or suspicious activity can become incidents that need to be removed before they damage the company’s reputation and finances.
Leaked company information and getting ransom notes showing that users’ and customers’ data was collected during a breach are some worst-case scenarios. They can be avoided with early interventions.
Comprehensive analytics in generated reports
One way that data risk analysis can help detect threats early is: by using AI and machine learning to compare attack surfaces.
By juxtaposing the current state of security with the previous one, the tool can infer whether there are signs of unauthorized use of credentials or other practices that violate the stated standard.
Findings after the analysis are summarized in the generated report. Comprehensive documentation of the latest security status is easy to understand and shows risks that may be difficult to trace manually.
Emphasis on high risks in reports
One feature that makes automated data analysis cost- and time-saving is that it separates the high-risk issues in the system.
In regular circumstances, IT teams are inundated with constant warnings. Many of these reports indicate low-risk threats that are unlikely to lead to major incidents right away.
Being aware of this can expose some important issues to members of IT teams who: throw them away as false positives†
The final summary highlights risks that need to be mitigated immediately by low-risk threats. In addition, it locates the part of the infrastructure that is most critical and offers possible solutions to fix the flaws.
Benefits of Automated Data Risk Analysis
Data risk analysis is a useful tool for your IT teams managing security as it can help them overcome the common pitfalls of manual analysis and risk assessment.
Common challenges teams face in risk assessments include:
- Lack of uniform terminology for key issues
- No universal approach to mitigating threats and flaws before they turn into incidents
What makes the lack of uniform threat definitions challenging is the variety of circumstances in which the problems arise. There is also a multifaceted understanding of security, depending on the members of your team.
Every business has different needs, attack surfaces, and cybersecurity needs. The context in which the risks arise will therefore differ from organization to organization. What can be a risky mistake for one company, does not have to lead to a major data breach for another.
In addition, every company has IT teams and cybersecurity experts with diverse backgrounds and points of view on the matter. For example, they may not agree on a single one definition of the term “risk”†
Ideas on how to fix bugs in the system and improve security may also differ. This can disrupt the dialogue between members of your team who need to react quickly to fix the problem in the system.
Automated risk assessments include the suggestions your teams can implement and strengthen security. They don’t have to follow them to the letter, but this option can facilitate decision-making processes when it matters most.
How does data risk analysis improve security?
In summary, data risk analysis uses AI and machine learning techniques to discover threats within the system in a timely manner.
The process is automated and can be customized based on the multifaceted needs of the business and the contexts in which the data circulates.
In addition, DRA is a useful tool for IT teams that are overwhelmed by the increasing workload and incessant alerts of every minor threat within the system.
Data risk analysis reveals high-risk risks that should be prioritized and reduces the chance of exposed systems and undiscovered breaches for months.