Cybersecurity AI: Integrating artificial intelligence into your security policy

Artificial intelligence is already being deployed and applied in a wide range of situations to boost productivity, increase sales, or improve user experiences. One area that AI use is still in its infancy is cybersecurity.

Rapidly evolving threat landscape

Yet, at a time when hackers’ ability to commit fraud and cause harm is more sophisticated than it’s ever been, leveraging every tool is paramount if you want to stay ahead of the curve. In addition, the average enterprise is seeing a steady growth in the number and type of users, devices, networks, and interfaces thanks to great strides made in cloud computing, the Internet of Things, 5G, network speeds, data volume, and other contemporary technologies.

When deployed in concert with other defensive mechanisms, AI can be a powerful weapon against cyberattacks. A growing number of businesses are tapping into AI not as a magical answer to all their cybersecurity needs but rather as one more tool in their cybersecurity arsenal. It’s all about making IT security more effective, more efficient, more manageable, and less risky.

Even hackers are turning to AI. For instance, spear phishing (a form of phishing where personalized messages are sent to users to trick them into divulging confidential information) is seeing widespread use. Not only does AI make spear phishing more efficient but it also allows execution to take place at a much higher rate than if the process was manually run. This, in turn, expands the potential attack surface.

Traditional signature-based security tools struggle to match up against this kind of attack environment. Such adversaries can arguably only be effectively contained by AI-powered security controls. Whereas the merits of AI in cybersecurity are clear, many enterprises continue to struggle to marry their IT security needs with the power of artificial intelligence. It can prove especially problematic scaling solutions from proof of concept to eventual full-scale deployment.

Planning a cybersecurity AI roadmap

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As with anything, the success of AI tools comes down to good planning. Whereas the detailed path to implementation will vary from organization to organization, certain essential steps have universal application when developing a roadmap. We cover these below.

1. Identify relevant and up-to-date data sources

Data is at the heart of any AI implementation and IT security is no different. To be effective, AI algorithms must be driven by the right data systems. The data must not just exist but should be current. After all, AI seeks to mimic human intelligence and should, therefore (ideally), be designed to continuously improve itself based on new knowledge. So, identifying the required data sets must be the first thing the business does in their quest to operationalize the new AI-driven cybersecurity algorithms.

2. Create data platforms to implement AI

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Identifying datasets is vital but will be futile if the information isn’t powered by an appropriate data platform. So concurrent with the identification of datasets, the business should set out to design an appropriate data platform. The platform should include automated quality checks that confirm the data used is current and secure.

3. Choose the right use cases to maximize benefits

To maximize the benefits of your AI cybersecurity investment, choose the right use cases for implementation. Note though that use case identification isn’t a one-off process. Rather, it’s a continuous exercise. AI use cases can be especially complicated since they seek to reduce or eliminate the amount of human intervention required for cybersecurity. You have to be prepared to run through several iterations and use cases before you eventually land on actionable and optimal output.

For best results, start with use cases that deliver tangible benefits but are relatively easy to implement. Focus on use cases where data is available, complete, current, and regularly refreshed. Make sure the project team includes subject matter experts who can interrogate the output from each use case so that the algorithm’s logic is tweaked accordingly.

4. Improve threat intelligence by collaborating with external parties

Collaborating with third-party security professionals or threat researchers is important. You can approach these experts directly or do so via online platforms that facilitate the crowd-sourcing of security information. This ensures the business is up to speed with the latest threats and can use this information to enhance the logic of their AI cybersecurity algorithms.

Larger organizations could come together and create their own proprietary platform where they share and discuss threat data with peers. Peers here could mean enterprises in the same area or the same industry. Understandably, peer platforms are rare due to the concerns about giving an advantage to the competition (since after all, they are in business).

Nevertheless, cybersecurity should bring all industry players together. If one business in an industry is attacked successfully, it increases the odds of other businesses in the same industry succumbing to the same threat.

5. Deploy SOAR technologies to enhance security management

Security orchestration, automation, and response (SOAR) are tools that help organizations collect security information from multiple sources. SOAR enables incident triage and analysis by combining human and machine capabilities. This allows the defining, prioritizing, and driving of incident response through a standard workflow connecting data sources and data platforms. SOAR is an essential component in optimizing the output of AI-based cybersecurity tools. It improves alert quality, reduces the time needed for onboarding cyber analysts, and improves security management.

6. Make cybersecurity analysts AI-ready

The IT security industry faces a significant gap between its needs and the number of qualified security experts in the market. This gap is even more pronounced in the AI space. Ergo, the process of deploying AI cybersecurity technologies should include training existing security staff and if need be, recruit new AI skilled personnel. Whereas AI implies diminished human intervention, an AI algorithm can only be as effective as the time and effort that goes into improving and perfecting it. The security experts would work in concert with process experts to get to the bottom of any issue and chart the best solution in the shortest time.

7. Establish AI cybersecurity governance for transparent, ethical, and long-term improvement

Effective cybersecurity isn’t possible in the absence of effective security governance. Businesses, therefore, must establish a comprehensive governance framework that underpins their AI cybersecurity strategy. Governance entails, among other activities, defining the roles of cyber analysts, monitoring algorithm output, detecting abnormal behavior, identifying the algorithm output’s risk tolerance, instituting a fallback plan if the algorithm fails, and defining performance indicators that objectively gauge AI success.

The cybersecurity AI race is on

An ever-changing technology landscape and ever-increasing attack environment have greatly complicated the security challenge for modern businesses. Many enterprises have already started exploring how cybersecurity AI can mitigate against these risks. For firms that are still wondering whether and how to implement AI for their security programs, it’s important to recognize the steps needed to deliver success. Doing that will help the business avoid needless losses and ultimately safeguard or grow company revenues.

Featured image: Pixabay

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