In recent decades, rapid technological development has transformed the ways we work, communicate, and live our day-to-day lives. However, this progress has been associated with the proliferation of malicious exploitation of systems, networks, and data by cybercriminals looking to take advantage of vulnerable systems.
An overview of modern cybersecurity landscape
The traditional cyber defense methods are growing less and less adequate against increasingly more sophisticated attacks. Cybercriminals have become proficient at using emerging technologies and are constantly developing new nuanced tactics to breach organisations’ security systems. Thus, to counter these advanced attacks, organisations are looking into emerging AI use cases in cybersecurity to strengthen their defenses.
It’s no wonder that the global AI in the cybersecurity market is booming, with organisations increasingly turning to AI-powered solutions to protect their networks and data. According to a recent report by Acumen Research and Consulting, the global AI in cybersecurity market size is estimated to reach a market value of USD 133.8 billion by 2030.
AI in cyber defense offers numerous advantages, from improved threat detection and prevention capabilities to cumbersome malware analysis automation. However, AI technology is complex, so implementing it can be challenging for organisations that do not have the necessary expertise or resources. Therefore, it’s important for businesses to understand the risks associated with AI-powered solutions before deploying them and ensure they have the right team to manage and monitor the implemented AI systems.
Below, we will discuss the major ways in which AI can be used to bolster corporate defenses, as well as considerations to take into account when implementing an AI-powered cybersecurity solution.
4 tips for successful implementation of AI in cybersecurity
Create a solid data governance system
The success of any AI-based system largely depends on the quality and quantity of data fed into it, and cybersecurity AI tools are no exception. To make AI a valuable cybersecurity asset, companies need to ensure that it has access to data from across the entire IT infrastructure. Yet setting this up often becomes challenging because different AI models require different data structures and formats.
To overcome this roadblock, make sure to consolidate all data feeds in one place before adding AI to your cybersecurity toolkit. The most effective way to standardise data is to employ a common data model (CDM).
Revamp incident response frameworks
Given the superior capabilities of AI in cybersecurity when compared to traditional methods, the number of threats that organisations face could increase exponentially. So quite often, security teams become overwhelmed with the sheer number of alerts after AI implementation and struggle to adequately prioritise and address them. This is why organisations must review their incident response methods and establish detailed response plans for every possible cyber threat.
Ensure the availability of sufficient talent
While AI implementation results in improved automation, it doesn’t mean that organisations need fewer security specialists. It’s quite the opposite. The implementation of AI calls for a new set of skills and expertise to manage AI-related tasks and workflows. Therefore, organisations must decide whether they want to retrain existing personnel or outsource security teams with specialised skills to maximise the value of AI-powered cyber defense.
First, organisations should consider their current team’s capabilities. If the team is already well-versed in cybersecurity and data literate, retraining them to work with AI is the best option. On the other hand, if the existing team lacks expertise or experience in cyber defense, outsourcing a specialised security team will prove more cost-effective.
Establish documentation practices
When it comes to AI adoption in the cybersecurity context, maintaining detailed documentation of how exactly AI integrates into IT infrastructure is essential. This includes:
- Storing logs related to the development of the system and establishing a standard revision control system to accurately track who made changes, when, and what they were.
- Maintaining compliance with GDPR and other relevant regulations and keeping a record of all audit logs to facilitate further audits.
- Keeping AI tools up-to-date through ongoing monitoring and regular maintenance. Since cyber threats evolve quickly, AI models should be able to adjust to new or changed conditions. This can be done by regularly monitoring the system’s performance, implementing patches when necessary, and ensuring that all models are continuously validated.
The bottom line
A successful implementation of AI in cyber defense requires a solid data governance system, reimagined incident response frameworks, sufficient talent and expertise to manage the new system, and established documentation practices. By taking these steps and ensuring that all stakeholders understand their roles and responsibilities within the new security framework, organisations can significantly boost their cybersecurity capabilities.
AI can be an incredibly powerful tool for enhancing cybersecurity but only when it is properly implemented. With thorough planning, preparation, and training, organisations can use AI’s unmatched potential to better protect themselves from today’s sophisticated cyberthreats.
Andrey Koptelov is an Innovation Analyst at Itransition, a custom software development company headquartered in Denver. With a profound experience in IT, he writes about new disruptive technologies and innovations.