Todays evolving threat landscape presents unique challenges to organizations looking to modernize their vulnerability management processes. By adopting a data-driven approach to risk analysis, implementing automated security monitoring, and customizing risk metrics to the needs of specific business stakeholders, organizations can ensure a comprehensive approach to addressing security vulnerabilities.
Data-driven approach
The Cybersecurity Threat Landscape (CTL) is an important tool to understand the current state of cyberattacks and vulnerabilities. It provides an independent view of observed trends and provides a high-level overview of the threats, techniques and procedures used by adversaries. It can view at both strategic and operational levels.
The CTL created by the EU Agency for Cybersecurity (ENISA) in partnership with various stakeholders. It produced using a wealth of data sources. Specifically, this includes information on past and present events as well as the trends influencing the future.
This intelligence-driven approach allows the authors to translate their audience’s needs into actionable recommendations. For example, a CTL report could include a mini threat landscape of notable incidents and a vertical or horizontal view of TTPs. This would allow the reader to understand the impact of a threat on the organization, and which tactics, techniques and procedures should target to mitigate it.
Automated risk analysis
Automated risk analysis is a key component of vulnerability management. It allows organizations to streamline and optimize their security program by delivering accurate quantitative analytics.
Modern organizations face an expanding attack surface, with more assets and vulnerabilities than ever before. This makes a proactive approach to risk assessment critical. It can flag issues before they become a security threat.
Risk assessments should conduct on a regular basis. This allows teams to make informed decisions on how much risk they’re willing to accept. Using automation, teams can streamline workflows and achieve greater return on investment.
The modern attack surface includes dynamic assets such as cloud services and containers. These new types of platforms can be difficult to track with traditional tools. As a result, organizations must utilize advanced Software Composition Analysis technologies to detect vulnerable open-source components.
Modern solutions utilize Artificial Intelligence to automate scanning of the asset landscape. In addition to analyzing the current state, they also validate the assets against baselines and best practices.
Customized risk metrics
In today’s ever-evolving threat landscape, security teams need a robust plan for vulnerability management. That plan must include key metrics that will help them reach their KPIs. Risk scoring is one such metric, and is an integral component of any modern vulnerability management program.
The risk scoring enables organizations to effectively manage their evolving attack surface. Risk scores can use to track and validate remediation prioritization and resource allocation. However, while risk scores can be useful, they do not solve every risk management problem.
Another metric to consider is risk tolerance. This is determined by an organization’s particular guidelines, as well as industry standards. As the number of assets and cloud workloads increases, more data required for effective risk management.
Using machine learning and analytics, modern techniques can also provide a comprehensive view of an organization’s attack surface. This data can analyzed to find vulnerabilities that are most likely to become severe threats. This will then enable an effective remediation plan.
Risk-based SLAs
Risk-based SLAs for vulnerability management are becoming more important as organizations face the increasing complexity of their attack surface. Modern networks are complex and include thousands of systems, operating systems, and geographic locations. As more cloud-based workloads and applications deployed, the threat landscape becomes ever more dynamic. In addition, new regulations and compliance requirements are pushing small businesses to expand their cyber security programs.
Today’s vulnerability management program needs to be flexible to accommodate these changing conditions. The traditional method for vulnerability management relying on human analysts is no longer adequate. Instead, automation of critical vulnerability management best practices can improve the reliability and efficiency of your program.
The risk-based approach to vulnerability management requires a comprehensive understanding of your organization’s vulnerabilities, and how those vulnerabilities affect your business. It also enables you to prioritize remediation efforts to mitigate risk. It can provide meaningful, data-driven recommendations for improving your remediation velocity.
When you have a holistic view of your organization’s vulnerabilities, you’ll be able to make more informed decisions about your business operations. For example, you’ll be able to limit access to your most critical systems. This can help you achieve your overall goal of cyber-resilience.
