5 ways AI is being used to improve security: Email security

Recent data regarding email attacks shows an increase in phishing attacks, business email compromise, and email-delivered ransomware. Spear-phishing attacks are more convincing, and all email attacks are increasing in frequency and sophistication. We can credit artificial intelligence (AI) and its many subsets for the rapid escalation of these threats.

Last week, we started a new blog series that will explore the top five ways that AI is being used in cybersecurity. We looked closely at threat detection and intelligence and the different ways that AI technologies have improved these functions. This week, we will explore the use of AI in email security, starting with a high-level overview of email protection.

Email protection

Email had been in use for over a decade before the Simple Mail Transfer Protocol (SMTP) was introduced in 1982. Email adoption was growing, and the industry needed standard email protocols, but there was very little work on email security at that time. Secure/Multipurpose Internet Mail Extensions (S/MIME) was introduced in 1995, and Pretty Good Privacy (PGP) came along in 1998. These were the first milestones in secure messaging.

DomainKeys Identified Mail (DKIM) and Sender Policy Framework (SPF) were introduced in the early 2000s. These were the early efforts to validate senders and prevent domain spoofing. Domain-based Message Authentication, Reporting & Conformance (DMARC) came along in 2012 and was embraced by big tech like Google, AOL, and Microsoft. This early adoption and the efforts of groups like the Anti-Phishing Working Group (APWG) have led to the widespread adoption that we see today.

Threats like spam, viruses, and phishing helped drive early inbox security measures. These solutions included rule-based spam filters, attachment and link scanning, and challenge-response systems.

Modern email security

Business email use is incredibly high, and most business professionals prefer to communicate by email over any other method. Barracuda research has found that 69% of ransomware attacks begin with an email, and the average total cost of an email attack in 2022 was $1 million USD. Email security is now a critical, mandatory aspect of cybersecurity, and AI plays a vital role in enhancing its effectiveness. There are several types of AI found in most email protection systems:

• Machine Learning (ML): ML algorithms learn from data and identify patterns to classify emails as benign or malicious. Supervised learning techniques, like decision trees and random forests, are commonly used for email classification.
  
  
• Natural Language Processing (NLP): NLP analyzes and understands human language in emails, enabling the detection of phishing attempts, spam, and other content-based threats.
  
  
• Deep Learning: Deep learning models, such as neural networks, can process large amounts of data and identify complex patterns in emails and attachments, improving threat detection accuracy.
  
  
• Anomaly Detection: AI algorithms identify deviations from normal behavior patterns, flagging potential security incidents for further investigation.
  
  
• Behavioral Analysis: Monitors user behavior to detect unusual activities that might indicate a security threat, such as unauthorized access or compromised accounts.
  
  
• Automated Incident Response: AI systems can automate responses to detected threats, such as quarantining suspicious emails or blocking access to compromised accounts.
  
  

These AI technologies work in tandem with ML and deep learning models processing large volumes of data, NLP analyzing language-based threats, and anomaly detection identifying deviations from baselines. Barracuda Email Protection includes several AI features that defend users from advanced threats and zero-day attacks. See Barracuda Phishing and Impersonation Protection for details on how Barracuda Email Protection uses AI to defend the business network.

AI and email security

Modern email security involves many intelligent systems and tools that are continually learning and improving. Here’s a look at how AI operates in different functions of email security:

The power of AI-enhanced email protection is based on continuous learning. These solutions always consume new data and learn from it to improve the defense of the email system. Real-time updates, like those mentioned in our post on threat detection and intelligence, ensure immediate protection against new threats. Some AI-enhanced systems, like Barracuda Email Protection, can accept user feedback directly from the inbox. This feature helps the AI model improve accuracy on false positives and missed threats.

This type of AI training is called automated model tuning. These AI-enhanced techniques are used to adjust and improve the machine learning models that defend the email system against threats. Automated model tuning keeps the security models updated and ready for the latest threats.

A holistic and preventative approach

To realize the many benefits of AI-powered email security, companies need to build a holistic and preventative approach to cybersecurity by enhancing the following areas:

Threat Detection: AI technologies excel at analyzing huge volumes of data and identifying patterns that may indicate potential threats. Machine learning models, anomaly detection algorithms, and natural language processing techniques are used to detect various cyber threats, including malware, phishing attempts, and insider threats.

Response: AI systems can initiate automated response actions when a threat is detected. These systems can quarantine suspicious emails, block malicious IP addresses, or alert security teams. AI can also prioritize and triage security alerts, which supports more efficient incident handling.

Recovery: AI-powered forensics and investigation systems can launch automated recovery workflows during or after a security incident. These systems can help security teams identify the root cause, determine the scope of the attack, and suggest remediation.

Continuous Improvement: AI systems should be optimized to get the most out of automated model tuning. Machine learning models can be retrained and updated in real-time, and should be configured to ingest user feedback, threat intelligence, performance data, and any other relevant information.

Barracuda can help

Barracuda provides a comprehensive cybersecurity platform that uses AI-powered security to defend all major attack vectors that are present in today’s complex threats. Visit www.barracuda.com for more on our award-winning security and data protection products.

Did you know...

Barracuda has published a new e-book titled Securing tomorrow: A CISO’s guide to the role of AI in cybersecurity. This e-book explores security risks and exposes the vulnerabilities that cybercriminals exploit with the aid of AI to scale up their attacks and improve their success rates. Get your free copy of the e-book right now and see all the latest threats, data, analysis, and solutions for yourself.