Role of Artificial Intelligence & Machine Learning in Incident Response

Date: 6 March 2024

Effective Cyber Incident Response Planning is critical to surviving the onslaught of cyber crime in 2024. Artificial Intelligence and Machine Learning in Incident Response applied along with a strong Cybersecurity Incident Response Plan can truly transform your cybersecurity maturity this year. 

The amalgamation of AI and ML in your Incident Response strategy can decrease time to detect, respond to and mitigate a cybersecurity incident. It can significantly streamline detection activities, reducing the need for manual intervention. The specialised IT/Incident Response teams are also empowered with better decision-making capabilities and data-driven insights through the use of these technologies.  However, it's important to note that AI and ML need humongous amounts of data to self-learn and be effective. And this is a challenge that you must be aware of before you make a leap into using these technologies.

In this article, we explore 5 of the most compelling ways in which Artificial Intelligence and Machine Learning can transform cyber incident response and elevate your overall cyber resilience tremendously. 

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Remember, before you decide to adopt any of the new technologies and tools to elevate your Incident Response, it makes sense to have your existing plans and processes reviewed by an expert. Don't just dive into expensive AI and ML tools until all the background work has been done.

Without a robust cyber incident response plan and relevant policies in place, just introducing AI and ML into the mix isn't going to help. Our NCSC Assured Training in Cyber Incident Planning and Response is the perfect way to go down this road. We delve into the subject of Artificial Intelligence and Machine Learning in Incident Response in considerable detail in this course. Attending the workshop will be the ideal way to understand if AI and ML technologies will help you in the stage of cyber resilience you're at.  

Our Virtual Cyber Assistant service is also specifically designed to cater to this need in a flexible and cost-effective way. Our cybersecurity consultants can help you review, refresh or create new cyber incident response plans and cybersecurity policies.

They can evaluate your existing cybersecurity protocols and recommend a structured approach to adopting AI and ML technologies in your existing infrastructure. They can also guide you to assess the strengths and weaknesses of your incident response plans through cyber attack tabletop exercises and cybersecurity audits and assessments. The results of these assessments can help you better determine where AI and ML will be a good, optimised fit for your organisation. 

Top 5 ways in which Artificial Intelligence & Machine Learning can enhance cybersecurity resilience    

1. Swifter Identification of Anomalies: Intrusion Detection Systems (IDS) monitor network activities and their job is to identify malicious activity or policy violations in time. The application of Artificial Intelligence and Machine Learning in IDS can significantly improve their ability to detect anomalies.  


AI and ML algorithms can go through the ever-growing business data volumes at tremendous speed. This can revolutionise the time to detect malicious activity. Application of the latest techniques such as supervised learning further bolsters the timely detection of any suspicious pattern changes. This can significantly minimise the window of opportunity for hackers as it greatly reduces the time they are allowed to spend in a network before being detected. 

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2. Faster & More Accurate Risk Prioritisation: With their ability to sift through massive volumes of data and the agility with which they can identify anomalies, AI and ML technologies can quite accurately predict your biggest organisational threats and risks at great speed. They can also categorise the risks as per the severity of their impact in case the risk turns into an actual incident.

This can provide the right focus points for Incident Response teams to prioritise their response efforts accordingly. Risk prioritisation, which is a big component in cyber incident response planning, can get a streamlined and fairly accurate direction with the application of AI and ML.     

3. Faster, Automated Response: Automation in cyber incident response helps organisations to respond to cyber attacks faster, more effectively. It also reduces the burden on Incident Response and technical teams so they can focus on the most critical aspects of managing an incident. 

They can also execute response actions based on pre-defined procedures. They can assign the right responder to a specific type of Incident. They may also establish a communication protocol based on the nature of the incident. 

More importantly, for specific types of incident, these technologies enable automation of the initial response steps.  For instance, if a network segment is compromised, automated response can mean that credentials access is immediately restored and patches are deployed without the need for human intervention. This can significantly reduce the amount of time that attackers have to cause damage.

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4. Efficient Recovery: By reducing the time to detect, isolate and take immediate response steps, AI and ML in cyber security incident response make post-incident recovery that much faster. They can also, in many cases, restore systems back to their last secure state. 

Efficient recovery plays a vital role in reducing costs from cybersecurity incidents for your business. You save costs by controlling the damage that the infection can cause if left to fester. You also save the money you would have otherwise lost due to business disruptions. You also hopefully avoid serious legal fees and regulatory penalties.     

5. Predictive analytics: A major part of good cyber incident management is documenting the incident response process and using that report to generate insights for improvements. Manually tracking the performance of your incident response plan and the team members is time-consuming, exhausting and often not extremely accurate. Automated technologies can help gather this information and process into a report with far less effort and far more accuracy. 


More importantly, however, AI and ML technologies can leverage the data generated in the new incident report to improve their predictive analytics. They can then forecast specific threats and risks with far greater efficiency and help you further finetune your cyber incident response strategy based on predictive analytics. 

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Final Word 

Artificial Intelligence and Machine Learning are quickly becoming indispensable in many walks of life. The same is true for cybersecurity. While it is true that there’s a long time before these sophisticated technologies will be able to helm an organisation through cybersecurity incidents on their own, they certainly help make the response process more efficient.

But don't forget the words of caution we started this article with - Without strong Cyber Incident Response Plans, NIST-based Incident Response Playbooks and a robust cybersecurity policy, jumping into AI and ML can be wasteful. The use of AI and ML can dramatically alter how you manage and mitigate the damage from a cyber attack, but only when you have done the groundwork for it.  

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