Why Rule-Based Systems Fail to Prevent Data Loss from Human Error

Rules are extremely easy to understand; however, when a system gets operationalized, it starts with 100 scenarios and then writes 100 rules to handle them. As time goes by, we encounter more and more exceptions and start making more rules to keep exceptions under control.

Unlike rule-based methods, machine learning is probabilistic, and uses statistical models rather than deterministic rules. The basic operation of a machine learning process is to say ‘given what we know about these historic outcomes, what can we say about future outcomes?'

What You'll Learn In This Webinar:

- What's wrong with rule-based systems, and how they can result in false positives or false negatives
- How machine intelligence is not only a proactive approach to data security, but more preventative than any alternative
- How machine intelligent security platforms allow for real-time threat reporting and risk analysis, demonstrating exactly what is being done in advance to keep data secure and prevent data loss.

Human error is the largest source of data breaches reported to the regulator ICO. While the focus is on threats caused by malicious external parties, like ransomware and malware, simple mistakes (like sending an email to the wrong person) are far more statistically prevalent.

Join this session to learn how machine learning can easily prevent your highly sensitive information being leaked outside of your organization, either maliciously or inadvertently.

About Tessian:
Tessian is a machine intelligent email security platform that automatically prevents cybersecurity threats like spear phishing, misaddressed emails, unauthorized emails (data loss) and non-compliance. Tessian makes email safe at some of the world’s largest enterprises across the financial, legal and technology sectors.