Insider threats are one of the most significant risks to modern businesses, and the problem is only likely to worsen. Job satisfaction is at an all-time low, inflation is increasing at an alarming rate, and many of the largest companies in the world are laying off workers en masse. The resulting environment is a breeding ground for insider threats.
Fortunately, the recent improvements in artificial intelligence (AI) and machine learning (ML) technologies mean organizations are better placed to combat insider threats than ever. This article will outline how organizations can mitigate insider threats with AI and ML.
Stage One: Data Collection
First, organizations must establish what an insider threat looks like in terms that an AI tool can understand. Data such as employee behavior logs, system logs, and access patterns help AI tools comprehend regular activity, serving as the basis for AI/ML models.
Stage Two: Feature Engineering
Feature engineering leverages the data collected in stage one to create new variables not in the training set. Organizations should extract features such as activity patterns, access frequency, file access permissions, and network traffic patterns to help them identify suspicious activity and identity insider threats.
Stage Three: Model Training
Organizations should utilize machine learning algorithms to train models on the collected and pre-processed data. For example, supervised learning algorithms – decision trees, random forests, and support vector machines – help classify normal or malicious behavior. Unsupervised learning algorithms, however, are best used to identify abnormal behavior patterns.
Stage Four: Behavior Profiling
By learning the typical patterns of system data and access, network activity, and other relevant behaviors, organizations can develop profiles of normal behavior for different user roles. Organizations should then input these profiles into their AI/ML tool, which will flag deviations from those profiles and warn of a potential insider threat. The best AI/ML tools combine behavior analysis with data analysis so that only anomalous behaviors regarding corporate, not personal or unimportant, data that security teams aren’t inundated with false alerts.
Stage Five: Real-Time Monitoring
Any AI or ML tool that detects insider threats must run around the clock and in real-time. If organizations only run their AI detection tools once a week, day, or even hour, the insider threat may have already succeeded by the time the security teams detect them. The system should compare ongoing activities against previously collected rules and behavior profiles.
Stage Six: Risk Scoring
Organizations should assign a risk score to users based on their behavior profiles and the threat any anomalies pose to the business. Assigning risk scores allows organizations to prioritize investigations and monitor individuals who might be a higher risk to the organization.
Stage Seven: User Authentication and Access Controls
Strong user authentication mechanisms such as multi-factor authentication (MFA) and biometric authentication ensure that users are whom they claim to be. Organizations should also enforce the principle of least privilege by only granting users the access rights necessary for their role. AI and ML tools will notify security teams if users try to access resources beyond their access rights.
Stage Eight: Incident Response and Remediation
The best AI/ML tools will not just detect insider threats but respond to them as well. If an insider threat is in progress, AI/ML tools will block data exfiltration across all channels, including cloud, email, websites, and removable storage devices.
Stage Nine: Continuous Improvement
Organizations must regularly update their AI/ML tools to reflect new data and evolving threats, including increasingly evasive malicious programs such as the recent Search Alpha virus. Monitoring the system’s effectiveness is essential for making performance adjustments as and when needed.
It’s important to note that AI tools can facilitate insider threats. Well-intentioned employees may experiment with AI tools such as ChatGPT to streamline business operations and demonstrate their ability to adapt to new technologies. However, any sensitive data provided to ChatGPT is at risk of becoming queryable or unintentionally exposed to other users. For example, some of the largest companies in the world, including Amazon and Samsung, have already experienced this firsthand.
While insider threats will undoubtedly plague organizations in the years ahead, AI and ML tools are promising methods of detecting and preventing them. Behavior and data analysis, risk scoring, strong user authentication and access controls, and real-time, continuous monitoring organizations can significantly decrease the likelihood of insider threats.
However, organizations must remember that AI/ML tools aren’t standalone solutions. To combat insider threats, security teams must integrate AI and ML tools into a comprehensive cybersecurity program that includes employee awareness training, policy enforcement, and other preventative measures.
About the Author: Josh is a Content writer at Bora. He graduated with a degree in Journalism in 2021 and has a background in cybersecurity PR. He's written on a wide range of topics, from AI to Zero Trust, and is particularly interested in the impacts of cybersecurity on the wider economy.
Source: Story.KISSPR.com
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