10 Tips for Updating Threat-Mitigation Systems with AI and Machine Learning

 

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Threat-mitigation systems are part of the larger network of cybersecurity protocols that protect your data and critical digital infrastructure. Updating these systems to account for changes in the cybersecurity landscape is crucial to the robust defence that these systems are meant to provide.

Thankfully, AI systems and machine learning models are leveraged to provide protection to these networks. As the malicious software and bad actors continue to evolve, so does the training data that is used to inform the behaviour of these programs to better shield the sensitive information and systems protected by the cybersecurity programs.

According to a recent expert interview from tech company NTT “Through machine learning, security systems can learn from data over time to become better at pattern recognition and identify threats more accurately. This can take the form of machine learning in analytics engines that are used to identify threats (so, learning from the system’s own experience) or machine learning from threat hunting by humans who look for and analyze anomalies missed by security software.”

With that objective in mind, what can be done to update these threat mitigation systems with AI and machine learning?

• Training, Training, Training

A software system is only as good as those that are tasked with working with it.

 

  

 
 

For your risk management system to work, your staff should be trained regularly and anytime that changes are made to the systems.

• Protect Against Data Poisoning

Machine learning systems are dependent on the training set of data that is used to educate their decisions. This can be tampered with in what is called a “data poisoning” attack. Be sure to use all appropriate measures to secure your training data and ensure it is not contaminated.

• Understand Adversarial Machine Learning

Adversarial machine learning involves using a machine learning system to extract information about the behaviours and characteristics of an ML model in order to manipulate the inputs to achieve a certain outcome. This can invalidate the efficacy of an ML model and must be accounted for by making your training algorithms resilient to adversarial action on a mathematical level.

• Ensure Industry Compliance

Industry compliance is the baseline of security protection you should have but it is still important to ensure compliance. You can use AI-powered software to monitor your compliance status and notify you of changes and the required action to take.

 		 

 
 

• Automate and Encrypt Data Backups

Data security is of paramount importance and the availability of the data is one of the pillars of secure data. Automate and encrypt data backups to provide reasonable access to all necessary data, protecting it from a catastrophic attack or failure.

 		 

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• Protect Against Model Extraction

Model extraction is a type of data attack that is done through legitimate queries to an ML model. It uses input data and data points received from the target ML model to determine the equations it uses to assume its answers or it's predictions. Use well-formed queries that determine the responses as one way of protecting yourself from this type of attack.

• Enforce Automated Strong Passwords and 2 Factor Authentication

Strong passwords and 2-factor authentication can halt an attack in its tracks. Use long passwords with symbols, letters, and numbers and if possible, use the automated, strong passwords provided by the AI system suggestions.

• Install AI-Powered Security Patches

Automate the installation of AI-powered security patches to shore up any holes in the firewall or any other part of the system.

• Ensure Physical Security of Access Points

Security risks are not solely in the digital world. The physical access points of a network are vulnerable to attack and must be protected. Use AI-powered facial recognition, passwords, and other methods to ensure access is only available to authorized users.

 		 

 
 

• Network Traffic Monitoring

Use ML models and AI-powered systems to monitor your network traffic for inconsistencies and potential threats to your network.

 

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