Trusting Artificial Intelligence in Cyber Security: A Double-Edged Sword

In 2019, Mariarosaria Taddeo, Tom McCutcheon, and Luciano Floridi published an article in Nature Machine Intelligence entitled, Trusting artificial intelligence in cybersecurity is a double-edged sword.


They argue that “trust in AI for cybersecurity is unwarranted and that, to reduce security risks, some form of control to ensure the deployment of ‘reliable AI’ for cybersecurity is necessary.” 


The same rings true today. While AI has come a long way since 2019, trusting AI blindly is a recipe for cybersecurity disaster. According to a report from McKinsey, cyber-attacks will increasingly be multiplied with the help of tools like AI, machine learning, and automation. Organizations will need to amplify their cybersecurity capabilities to eliminate multiple automated threats to spam systems and overwhelm them. 


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How AI Can Help Cybersecurity Teams Detect Cyber Threats

AI is extremely powerful and can allow cybersecurity teams to combat attacks through automated detection and blocking. The technology can help to secure networks and services whenever an anomaly occurs. AI can prevent unknown threats by constantly combing the web and learning from what it finds. With the sophistication of attacks increasing, more traditional security methods may not be enough. AI can be an excellent tool to extend your cybersecurity team’s threat detection and overall security scope through:

  • Vulnerability Management
  • Network Monitoring
  • Threat Detection
  • Improved Security Over Time


Vulnerability Management

AI can proactively manage network vulnerabilities before they harm your network. AI and machine learning together use extremely complex algorithms to analyze suspicious patterns to enhance network security overall.


Monitor Your Network

Ai can set up a network’s baseline and protect it constantly based on traffic patterns. It uses the baseline as a reference point and adjusts as traffic patterns change over time. 


Threat Detection

AI uses behavioral analysis to monitor all network activity constantly and assist with threat detection. It can identify malicious activities on an ongoing basis and respond to threats immediately.


Improved Security Over Time

Because AI constantly learns, it improves over time. The sooner you deploy an AI cyber security solution, the better it will be. Plus, it tends to learn exponentially faster. So while it may seem like a big investment when getting started, you know your system will continually improve. 

Risks of Using AI in Cybersecurity

While AI can be a wonderful asset for your cybersecurity team and your system overall, some risks are associated with using it, and some best practices to mitigate those risks. 

Here are a few risks to relying on AI for Cybersecurity:


  • The quality of data
  • Over-reliance on Models
  • Security Breaches
  • Unintended Consequences


Quality of Data

Poor quality data can lead to inaccurate results from AI models. Poor data quality can be caused by incomplete or incorrect data, leading to problems such as false positives or negatives, which can put organizations at risk. It is important for organizations to ensure that their data is clean and accurate before feeding it into an AI model.


Over-Reliance on Models

Organizations should not rely solely on models for their security strategy as models are only as good as the data they receive. Human analysts must be included in the process to help ensure that models do not miss anything or make wrong assumptions based on the available data.


Security Breaches

AI-based systems may be vulnerable to hacking due to their reliance on large amounts of sensitive data, which attackers could attempt to access or manipulate. Organizations must ensure that all security measures are in place and regularly updated when using AI-based systems to minimize potential risks associated with malicious actors trying to gain access.


Unintended Consequences

In addition, there is always a potential risk that an AI system may have unintended consequences due to unforeseen circumstances, such as unknown vulnerabilities or emerging threats that no one has anticipated before. This could potentially cause major damage if not addressed promptly, so organizations should have contingencies planned out in advance just in case something unexpected occurs while using AI for cybersecurity purposes.


Related Content:  5 Benefits of Firewall Automation


Ultimately, as the scholars that pointed out the double-edged sword that is AI in the cybersecurity context pointed out back in 2019, the concept still rings true. 


The Compuquip team of MSSP engineers is here to help you, and your organization develop a comprehensive cybersecurity strategy involving AI and machine learning but with protection. Contact us today, and we’ll be happy to help!


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