It’s unfortunate that any technological progress gained comes with an increased risk of security breaches and vulnerabilities. This tight-rope walk is a massive challenge as multi-cloud environments have thousands of layers of code and app sprawl – which creates even more attack surfaces.
Put simply, the task of monitoring the flood of security threats is too big for humans to handle. However, we have seen the emergence of predictive AI to help reduce the ever-increasing cybersecurity challenges.
One example of how predictive AI can help is its ability to automatically recognize unidentified computers, servers, and code repositories on a network. Another is how it can monitor the dark web for crucial linguistic patterns exhibited by hackers uploading new threats and immediately notify security personnel.
In this analysis, I’ll share more ways predictive AI is being applied in security and some of the initial successes.
Automated Scanning and Security Risk Reporting
Monitoring network security properly requires processing massive amounts of data located across disparate locations which are often unlabeled and unstructured. Imagine the never-ending cycle of trying to collect all this data, logically organize it, and then analyze it for any potential threats.
Even if you hired a large team to work on this and managed to pull some of the data together, you could still miss critical insights and open the door for future vulnerabilities.
Properly configured Predictive AI can automatically monitor, categorize, and alert cybersecurity teams of potential threats. This allows these teams to harden security policies and mitigate future attacks.
For example, Fortinet uses AI to learn of behavioral tactics of threat actors “from earliest stage reconnaissance and weaponization, all the way through to the cybercriminal’s ultimate action on objectives.“
Data-Driven Decision-Making and Incident Response
If you aren’t using data to drive your strategic cybersecurity decisions then it’s like trying to find a slightly different needle in a needle stack. Not only would this harm you as you would be making poor decisions from the lack of data, but you would also make your company an easy target.
I strongly recommend using predictive analytics as a method to provide you with more data-anchored options and significant insights. Also, many of the solutions available today are easily incorporated into third-party services to further safeguard your data.
As an example, artificial intelligence can be used throughout a contract management process to not only help standardized practices but spot any anomalies or potential threats from attached files, suspicious links, or bot intrusion. This approach brings business operations and cybersecurity together to mitigate business risks and security risks.
Enhanced Risk Forecasts
Incorporating predictive AI’s self-learning capabilities into a business’s existing workflow is a highly effective way to discover new abnormalities, evaluate the associated risks, and generate accurate risk forecasts for the future.
For instance, if you’ve recently integrated social media into your customer service processes, your predictive AI’s risk-detecting capabilities can help you spot any problems. But, it can take things a step further by delivering forecasts on future threats. These insights provide context, where you should shift your focus, create coaching opportunities, and if any additional intervention is needed.
Initial Successes
Microsoft and Google are seeing huge success with their AI-based malware detection, phishing campaign measures, and information monitoring. For example:
- Microsoft’s Cyber Signals program uses AI to analyze trillions of security signals and surfaces the cyberthreat intelligence to drive executive decisions.
- Millions of dollars have been invested by federal funding agencies like National Science Foundation to develop cutting-edge AI tools for extracting useful insights from data produced by the dark web.
Final Thoughts
It’s safe to say that AI will continue to be used to as an effective tool to safeguard businesses and vital systems from cyber threats. However, I would strongly urge you to create a multi-pronged strategy for security that doesn’t ignore the role of human intelligence.
The only path forward in the fight against sophisticated criminal attacks is to combine the skills of human cyber specialists with those of intelligent machines.
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