AI-Driven Patch Management Is Driving the Future of Cybersecurity

  • AI-Driven Patch Management Is Driving the Future of Cybersecurity

The more sophisticated tradecraft of cybercriminals now includes more contextual knowledge about the most susceptible CVEs. The ultimate consequence is exposed attack surfaces with exploitable memory conflicts caused by manual patch management techniques or overstuffing endpoints with agents.

Attackers are still polishing their art, weaponizing vulnerabilities with cutting-edge methods and tools that may avoid detection and outwit manual patch management systems.

According to CrowdStrike's 2023 Global Threat Report, up to 71% of all detections indexed by the CrowdStrike Threat Graph include malware-free incursion activity. Security flaws that had not yet been addressed caused 47% of breaches. Manual security vulnerability remediation is used by 56% of organizations.

Consider this if you still need further evidence that manual patching techniques are ineffective: After remediation, 20% of endpoints are still not up to date on all patches, making them exposed to breaches once more.

The chief product officer of Ivanti, Dr. Srinivas Mukkamala, stated that patching is not at all as easy as it may seem. Even well-resourced IT and security teams struggle to set priorities when there are other urgent needs. Organizations must use a risk-based patch management system and use automation to discover, prioritize, and even resolve vulnerabilities without excessive manual involvement if they want to minimize risk without increasing effort.

Vendors fast-tracking risk-based vulnerability management and AI

A portion of AI-based patch management is built on algorithms that require a constant flow of data to continue "learning" and evaluating fix vulnerabilities. To set the pace of the industry, look for top providers who have developed their AI and machine learning capabilities over numerous product generations.

The GigaOm Radar for Patch Management Solutions Report highlights the technological benefits and limitations of the leading patch management providers. This study is noteworthy since it assesses each vendor and contrasts providers in the market categories covered by patch coverage and deployment strategies. Among the businesses evaluated in the research were Atera, Automox, BMC Client Management Patch powered by Ivanti, Canonical, ConnectWise, Flexera, GFI, ITarian, Ivanti, Jamf, Kaseya, ManageEngine, N-able, NinjaOne, SecPod, SysWard, Syxsense, and Tanium.

It takes a breach to break a reactive checklist mentality

According to CISOs from top insurance and financial services companies who spoke anonymously, the pressure to patch endpoints and mission-critical systems usually doesn't arise until after a system has been compromised because of endpoints that have outdated patches. One CISO recently admitted to VentureBeat that it's a reaction that is reactive rather than prescriptive. Frequently, the essential patching work is not prioritized until a large event occurs, such as an incursion, a breach of a mission-critical system, or the discovery of stolen access credentials.

According to Ivanti's State of Security Preparedness 2023 Report, what CISOs are saying makes sense. According to Ivanti, external events, intrusion attempts, or breaches lead to the restart of patch management efforts 61% of the time. Despite the fact that businesses are rushing to protect themselves from cyberattacks, the sector still adopts a reactive, checklist attitude. In spite of the fact that more than 90% of security experts indicated they prioritise fixes, they also said that all types score highly, therefore none does.

5 ways AI-driven patch management is shaking up cybersecurity

An ideal use of AI in cybersecurity is automating patch management while using various datasets and integrating it into an RBVM platform. Leading AI-based patch management solutions can decipher data from vulnerability assessments and rank risks according to patch type, system, and endpoint. AI and machine learning are being accelerated by almost every vendor in this sector due to risk-based scoring.

When prioritising and automating patching operations, AI- and machine learning-based vulnerability risk assessment or scoring provide the knowledge security teams require. The top five ways that AI-driven patch management is transforming the future of cybersecurity are as follows:

1. Accurate real-time anomaly detection and prediction — a first line of defense against machine-speed attacks

To overpower endpoint perimeter-based protection, attackers depend on machine-based exploitation of patch vulnerabilities and flaws. Attack patterns are identified and added to the algorithms' knowledge base via supervised machine learning techniques that have been trained on data. As a result of the fact that machine identities now outweigh human identities by a ratio of 45, attackers look for vulnerable endpoints, systems, and other assets that are not patched up to date.

With more than 160,000 vulnerabilities already known, it is understandable, according to Mukkamala, that IT and security experts find patching to be excessively difficult and time-consuming. In order to help teams prioritise, validate, and implement fixes, organisations must use AI technologies. In order for IT and security teams to concentrate on critical business objectives, the future of security involves delegating routine, repetitive work to AI copilots.

2. Risk-scoring algorithms that continually learn, improve and scale

Because it requires juggling several unknown restrictions and programme dependencies at once, manual patching frequently fails. Take into account every aspect a security team must manage. Vendors of enterprise software are sometimes tardy to release fixes. Regression testing could have been lacking. Rush-released patches frequently cause other components of a mission-critical system to malfunction, and suppliers frequently have no idea why. On endpoints, memory conflicts are another common occurrence that compromises endpoint security.

The use of risk rating in patch management automation is crucial. The highest-risk systems and endpoints are prioritised and managed with the use of vulnerability risk ratings. Risk-scoring systems have been created by companies like Ivanti, Flexera, Tanium, and others to facilitate AI-based patch management.

3. Machine learning is driving gains in real-time patch intelligence

One of the most useful technologies for enhancing vulnerability management across large-scale infrastructure is machine learning. Algorithms for supervised and unsupervised machine learning aid in achieving SLAs more quickly. They boost the effectiveness, scope, and speed of event processing and data analysis. Additionally, they support anomaly detection. Using patch intelligence, machine learning algorithms may discover system vulnerabilities and stability problems for thousands of updates. They are useful in fending off security threats because of all this.

Automox, Ivanti Neurons for Patch Intelligence, Kaseya, ManageEngine, and Tanium are leaders in this field.

4. Automating remediation decisions saves IT and security teams valuable time while improving prediction accuracy

Machine learning algorithms continually analyse and learn from telemetry data to increase forecast accuracy and automate remedial choices. The quick evolution of the Exploit Prediction Scoring System (EPSS) machine learning model, developed with the combined knowledge of 170 professionals, is one of the most exciting aspects of this breakthrough sector.

The EPSS is designed to aid security teams in managing the rising tide of software vulnerabilities and spotting the most perilous ones. The model now in its third iteration outperforms earlier iterations by 82%. Tracking the Right Vulnerability Management Metrics by Gartner (client access needed) states that "remediating vulnerabilities by faster patching is costly and can misdirect the most active threats."

5. Contextual understanding of endpoint assets and identities assigned to them

How rapidly companies are increasing their use of AI and machine learning to find, inventory, and patch endpoints that need updates is another exciting area of AI-based patch management innovation. Although each vendor takes a different strategy, they all want to replace the antiquated, erroneous, manual inventory-based method. Providers of RBVM platforms and patch management are moving quickly to provide new versions that enhance prediction accuracy by making it easier to determine which endpoints, workstations, and systems need to be patched.


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