Today’s patch management practices are increasingly unable to keep pace with the advancing sophistication of cyber threats. The reliance on manual patching and a reactive approach has left many organizations vulnerable to breaches. This has been further evidenced by the fact that over half of organizations still remediate security vulnerabilities manually, and about 47% of breaches are a result of unpatched security vulnerabilities.
For IT security departments, it’s clear that AI and machine learning are becoming key components in evolving cybersecurity strategies.
Risk-based vulnerability management (RBVM) and AI-driven patch management systems are showing promise as game-changers, however. These technologies, which use AI to assess and prioritize patch vulnerabilities, are becoming a part of the tech stack consolidation plans for many organizations. They provide greater efficacy and can be deployed quickly because of their cloud-based nature.
An important aspect of these AI-driven systems is their ability to interpret vulnerability assessment telemetry and prioritize risks by patch type, system, and endpoint. This risk-based scoring is why AI and machine learning are being embraced by nearly every vendor in this market. These technologies provide the insights that security teams need while also automating patching workflows.
Moreover, AI-driven patch management systems are transforming cybersecurity through accurate real-time anomaly detection and prediction, risk-scoring algorithms that continually learn and improve, and machine learning-driven gains in real-time patch intelligence. Additionally, automating remediation decisions saves valuable time for IT and security teams while enhancing prediction accuracy.
These new AI systems provide a contextual understanding of endpoint assets and the identities assigned to them, replacing the outdated, error-prone manual inventory-based approach. They also apply machine learning algorithms throughout the lifecycle to improve version control and change management at the application level.
To fully harness the potential of AI-driven patch management, organizations must shift from a reactive to a proactive approach. Instead of waiting for a breach to occur before prioritizing patch management, organizations must integrate these advanced technologies into their cybersecurity strategies. In a world where cyber threats are ever-evolving, AI-driven patch management offers a promising solution for enhancing security and staying a step ahead of potential breaches.
Here’s how AI-driven patch management is transforming the cybersecurity landscape:
- Real-time Anomaly Detection and Prediction: AI and ML are being used for real-time anomaly detection and prediction, providing an initial line of defense against high-speed cyberattacks. Through supervised learning, AI systems can identify attack patterns, and with machine identities outnumbering human ones, opportunities for breaches in unprotected systems are abundant. With an increasing number of vulnerabilities, AI assists in automating patch management, freeing up IT and security teams to focus on strategic initiatives.
- Risk-scoring Algorithms: AI and ML also aid in developing risk-scoring algorithms that continually learn, improve, and scale. Manual patching often fails due to various constraints and dependencies, but risk scoring helps automate patch management by assigning vulnerability risk ratings to prioritize high-risk systems and endpoints. Companies like Ivanti, Flexera, and Tanium have developed risk-scoring technologies to streamline AI-based patch management.
- Real-time Patch Intelligence: Machine learning is proving invaluable in improving vulnerability management across large-scale infrastructures. It helps achieve faster Service Level Agreements (SLAs), increases data analysis efficiency, and aids in anomaly detection. Machine learning algorithms can provide threat data for numerous patches, revealing system vulnerabilities and stability issues. Leaders in this space include Automox, Ivanti Neurons for Patch Intelligence, Kaseya, ManageEngine, and Tanium.
- Automated Remediation Decisions: Machine learning algorithms improve prediction accuracy and automate remediation decisions by continuously analyzing and learning from telemetry data. An innovative development in this area is the Exploit Prediction Scoring System (EPSS) machine learning model, which helps manage software vulnerabilities and identify the most dangerous ones. The latest iteration of this model performs 82% better than its predecessors.
- Contextual Understanding of Endpoint Assets and Identities: AI and ML are rapidly enhancing their abilities to locate, inventory, and patch endpoints requiring updates, replacing outdated, manual inventory-based approaches. Vendors are developing new releases that increase predictive accuracy, improving the ability to identify which endpoints, systems, and machines require patching.
Thus, AI and ML are reshaping the future of cybersecurity by automating patch management, interpreting vulnerability assessment data, and prioritizing risks, thereby enhancing system security and reducing manual workloads.