Cyberattacks are increasing in frequency and severity. This means maintaining the security of IT systems is more critical now than ever – and doing so effectively requires innovative solutions. As a result, many companies are turning to AI – which is set to revolutionise patch management.

Effective patch management is a time and resource-intensive task, and as a result, many businesses struggle to keep pace with the sheer quantity of patches released by software suppliers. It’s a case of balancing patches to stay secure while maintaining system stability, which can be especially daunting for businesses with a large and complex IT infrastructure.

How can AI streamline patch management?

AI is set to revolutionise patch management, bringing with it the capacity to process vast amounts of data, analyse patterns within this data and support informed decision-making.

AI can streamline processes and reduce the time and resources required to keep up with patch management and ensure security. This enables businesses to allocate their IT resources more strategically so that they’re centred around activities that require human input.

AI in patch management offers a number of benefits, including:
• Continuous monitoring – 24/7 monitoring of your systems to identify any new vulnerabilities or patches to reduce the window of exposure to potential threats.

• Predictive analysis – the ability to predict the impact of a patch on a system’s performance before it’s implemented, reducing the risk of downtime or unintended consequences.

• Customised patch prioritisation – assessing an organisation’s unique IT system and applying this understanding to prioritise patches based on their potential impact, criticality and relevance.

• Automated vulnerability detection – applying historical data to predict the system vulnerabilities most likely to be exploited to inform patch prioritisation.

• Automated testing – testing the impact of patches before they’re applied to ensure they don’t conflict with existing software and create unexpected issues elsewhere in the system.

• Adaptive learning – learning from past patch management processes to improve the efficiency and effectiveness of an organisation’s patch management strategies.

Potential challenges of AI in patch management

However, using AI in patch management doesn’t come without its challenges. Primarily, as is the case wherever AI is implemented, is that the technology comes with a steep learning curve. There’s a need for new skills to understand how, where and when to apply AI for patch management. This brings the need for investments in outsourced expertise or training to upskill existing team members. There are also ethical considerations to be made, especially if the technology is being used to make autonomous decisions about patch prioritisation and implementation.

Additionally, as anyone who’s used AI in the past will know, it’s not perfect. Its predictions aren’t always accurate, and it may not always correctly predict the impact of patches, which can lead to false positives or negatives within the system.

The future of AI in patch management

As AI continues to evolve, its impact on patch management will become increasingly prevalent. However, when considering how to use AI in this way, it’s important for organisations to consider the IT infrastructure’s complexity, AI training requirements and the balance between automation and human oversight.

Effective patch management isn’t easy – and organisations need to find a balance between trying to keep up without automation and applying AI too quickly and getting it wrong. There’s a need to have someone with dedicated expertise – whether internal or external – who can help oversee and manage AI’s implementation to maximise its potential for patch management.

AI has the potential to completely transform patch management from a resource-intensive task to an automated, data-driven process. When applied carefully, AI can ultimately help organisations to enhance their security and optimise their IT resources.

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