A single vulnerability can expose an entire system to a devastating data breach. Many of the most successful modern cyberattacks exploit known vulnerabilities, and these attacks can be prevented by employing effective vulnerability management practices.
Artificial intelligence drives the next generation of vulnerability scanning in the modern business landscape.
Here’s what this means for your business.
What Is Vulnerability Scanning?
Vulnerability scanning is the process of assessing and identifying the security risks that a system or network faces. This forms a foundational component of cybersecurity strategy as it helps organizations prevent attacks and improve their general security posture while ensuring full compliance with applicable regulations.
It is a continuous and proactive approach to ensuring an organization’s digital assets remain secure. Despite an evolving threat landscape, traditional vulnerability scanning methods are still effective but have some significant limitations.
For example, they are very time-consuming and resource-intensive to implement. Moreover, they are prone to false positives. As modern IT environments grow increasingly complex, they may need help to provide an acceptable level of efficiency and effectiveness.
Fortunately, artificial intelligence is a valuable tool in vulnerability scanning and is widely expected to usher in a new era of effective vulnerability scanning.
By leveraging natural language processing, machine learning, and other modern techniques, it can optimize and automate multiple components of the vulnerability scanning process.
How AI Can Streamline Important Aspects Of Vulnerability Scanning
AI facilitates every step of the vulnerability scanning process.
Discovery
The primary step in any vulnerability management effort is identifying vulnerabilities, typically achieved by using tools that can compare a system’s configurations and software versions to a database of known vulnerabilities.
Artificial intelligence can help cybersecurity professionals identify and map the devices and assets that make up a network, including transient, unmanaged, and hidden components, classifying and prioritizing them according to their exposure and importance.
Analysis
AI is useful for analyzing and interpreting the vast amounts of data collected from vulnerability scans. This empowers organizations to identify vulnerabilities, configurations, and compliance issues that need to be remedied.
In addition, AI’s impressive processing power enables it to collate and contextualize data drawn from multiple sources, including asset inventories and threat intelligence feeds.
Remediation
Once vulnerabilities have been identified, AI can recommend and execute the most effective remediation actions.
For example, AI can determine the best path forward if a business needs to change configurations, isolate specific devices, or apply patches. In addition, it can monitor the effectiveness of each action and offer feedback to ensure ongoing improvements.
Why AI is Superior to Traditional Vulnerability Scanning Methods
Here are the top reasons AI outperforms traditional vulnerability scanning methods.
Automation
Traditional approaches to vulnerability management are highly dependent on manual processes. Not only is this inefficient, but it is also prone to human errors and can be very time-consuming.
AI’s automated nature speeds up the process while eliminating the risk of missed vulnerabilities and other oversights.
Cost-Effectiveness
Although AI requires an investment, it is considerably more cost-effective than remedying the aftermath of a successful security breach, which may involve paying cybersecurity professionals overtime, purchasing new systems, paying fines, and hiring experts to restore the business’s reputation.
Adaptability
One major shortcoming of traditional approaches is the need for help to stay on top of emerging vulnerabilities and rapidly emerging threats.
AI enables organizations to avoid manually analyzing security reports, providing quick feedback and swift responses to new risks.
Contextual Analysis
Traditional vulnerability scanning entails a narrow focus on identifying specific vulnerabilities. AI, in contrast, can consider the broader context of the business’s infrastructure and priorities, eliminating inefficiencies and poorly aligned remediation solutions.
Broader Scale
Modern IT environments constantly adapt to the current business landscape and technological innovations. AI can quickly scan and assess vulnerabilities no matter how much the business’s demands increase or change, ensuring systems are not exposed as the business grows and evolves.
Intelligent Prioritization
AI algorithms can analyze vulnerabilities according to their likely impact on the business, the chances of the vulnerability being exploited, and its overall severity.
This allows them to prioritize vulnerabilities intelligently so that cybersecurity teams can focus on addressing the most pressing issues first, maximizing the efficacy of remediation efforts.
Self-Learning
AI-powered systems are constantly learning from data and adapting to evolving threats. This allows them to improve their effectiveness and accuracy in identifying vulnerabilities and providing feedback over time, ensuring they are always prepared to address new and emerging threats.
Learn More About The Benefits Of AI-Supported Vulnerability Management
By implementing robust vulnerability management processes driven by AI, organizations can enjoy proactive risk management and protection against potential attacks.
This ensures their sensitive data is protected while boosting customer trust and reputation. To learn more about protecting your business from cyberthreats, request a consultation with the vulnerability management professionals at Advantage Tech today.
