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How does AI empower information security and strengthen enterprise security?


AI - Security

"Information security" is currently a major concern for many companies; as they face market changes, competition from international manufacturers, human resources deployment and other challenges, the significance of information security cannot be overstated.


Current situation of information leakage


Recent worrisome information security incidents include an anonymous hacker's claim on 'Breach Forums,' an online hacker forum, stating they stole personal data from up to 1 billion Chinese citizens from Shanghai's public security system. The incident, if proven to be true, would be one of the largest data breaches in history and has raised concerns among tech experts. This incident underscores the importance of information security and serves as a reminder for enterprises and organizations to protect user data more effectively in the increasingly digital era, where data leakage threats are growing.


Fortunately, as digital transformation among enterprises matures and artificial intelligence technology advances, automated machine learning (AutoML) has gained significant attention. It provides us with a new solution to the problems we face in the new era. Next, we will share how Profet AI utilizes AutoML to assist the information security department in identifying potential security issues within the company using artificial intelligence.


AI-Security-1

2021, source from Varonis System https://www.varonis.com/blog/data-breach-statistics


Limitations of traditional practices


Traditional rule-based systems rely on binary "yes" and "no" judgments for risk assessment and rely on the experience of IT personnel to detect potential risks one by one. However, this approach has limitations, including the complexity of rule formulation, the possibility of overlooking potential risks, and the completeness of rule enumeration. Furthermore, transferring IT personnel's experiences into systematic procedures can be challenging. Fortunately, the introduction of AI technology can address these limitations, enhance previous audit methods, and provide more assistance in risk assessment.


Application and benefits of AutoML in information security


AutoML plays a crucial role in enhancing information security by addressing the limitations of traditional rule-based systems. Here's a summary of its applications and advantages:

  1. Filling the gaps in traditional rules: AutoML employs big data modeling to complement traditional rule-oriented systems, resulting in a more comprehensive risk reduction strategy.

  2. Identifying email security risks: AutoML models are constructed using historical Mail Log data from the company's internal information system. These models predict the risk associated with emails sent by internal personnel, enhancing email security.

  3. Monitoring key risk indicators: AutoML establishes anomaly detection indicators for personal data access (IPG/DG), enhancing data security. These indicators are crucial for monitoring data-driven decision-making.

  4. Data-driven decision-making support: In the past, decisions were mostly made based on experience. AutoML can assist information security personnel to provide data-oriented decision-making suggestions; it also can further explore key factors that affect decision-making as well as monitor leading indicators.

  5. Sensitive information classification: The information of key departments of the company usually stays confidential and sensitive. In the past, when there were no tools, it was quite difficult to carry out effective classification and monitoring. AutoML tools can assist enterprises in classifying sensitive information, addressing a historical challenge.

Case study: AI screening for email security concerns and risk prediction


In many cases, information security incidents within a company have already occurred before they are discovered, resulting in significant damage. AI models can greatly enhance the company's approach to inspections, making them more efficient and effective, rather than relying on random inspections by individuals in the past. For instance, consider a company with a monthly email volume in the tens of thousands, where internal auditors used to conduct weekly random inspections of hundreds of emails, leaving many uninspected. With AI, past data can be modeled to predict and identify high-risk emails, ensuring that every email is thoroughly inspected. This approach minimizes the scope of potential risk and enhances overall security.


As model usage matures, new features can be continuously added. Through initial employment and the experience of experts in this domain, the company's in-house security experts may add new features to optimize the model for greater accuracy. If one is unsure how to perform additional features, Profet AI also provides after-sales consulting services. Companies can further optimize the model based on the flexibility of their platform by discussing with Profet AI’s professional consultants and the company's in-house security experts.


If achieving these goals seems challenging at this stage, there is no need to worry. Profet AI’s AutoML provides "Feature Engineering," which automatically converts email data into more than 40 features and provides the necessary training data for modeling. We also offer the "AI Hackathon" workshop partnership; through the workshop, companies can collaborate with Profet AI's professional consultants to enable employees and domain experts to swiftly understand AI concepts and applications through general education, security issue identification, and hands-on modeling courses. Most importantly, the partnership aims to develop the concept of "data application" and methods to effectively utilize prediction models, so that the culture of AI and data-driven decision-making can take root within the companies and eventually achieve tangible results of AI digital transformation.


 



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