Today, email-based attacks are increasingly sophisticated, however artificial intelligence (AI) can offer vital defense. With AI use, organizations can counteract advanced phishing, ransomware, and business email compromise (BEC) schemes that easily evade traditional defenses.
The below are just a few ways of how you can effectively use AI to strengthen email security.
- Understand the Key Email Threats AI Can Help Prevent
Before diving into AI applications, it’s important to recognize the major email security threats your organization faces. These are the biggest:
- Business Email Compromise (BEC): These attacks involve fraudsters impersonating executives or trusted partners, tricking employees into making unauthorized financial transactions.
- Phishing: Attackers use deceptive emails, often pretending to be legitimate organizations, to steal credentials or sensitive information.
- Ransomware: Delivered through phishing, ransomware encrypts data until a ransom is paid, posing a serious risk to business continuity.
- Data Loss: Phishing is a significant cause of data breaches, especially in sectors handling sensitive information.
By knowing which threats are most prevalent, you can focus your defenses and AI-based approaches accordingly.
- AI Models in Email Security and How They Work
AI and machine learning (ML) work by identifying patterns, behaviors, and anomalies in email data, which helps distinguish between legitimate and malicious emails. Here’s a breakdown of AI applications in email security and how you can implement them:
- Pattern Recognition Models
Machine learning models excel in detecting recurring patterns, especially in phishing and social engineering emails. These models look for indicators such as:
- Sender reputation: ML assesses past interactions with the sender, looking for anomalies.
- Communication patterns: Consistent patterns in subject lines, greetings, or requests are flagged when disrupted by unexpected wording or unusual requests.
Most cloud email protection solutions, for instance, apply pattern recognition to identify and block phishing attempts in real time. This helps organizations detect deviations in messaging and communication style that indicate an attack.
- Content Analysis Using Natural Language Processing (NLP)
Using NLP, AI analyzes the content within an email—including attachments and embedded links—to assess its intent and authenticity. Large Language Models (LLMs) can interpret text patterns, distinguish between normal language and social engineering techniques, and detect impersonation attempts.
Here’s how to implement content analysis:
- Train NLP models on email data to classify messages as legitimate or suspicious based on word choice, tone, urgency markers, and other linguistic cues.
- Deploy NLP-powered email filters to screen for phrases that suggest social engineering, like “urgent payment” or “request for financial information.”
Many secure email gateways use this approach, where NLP and machine learning scan messages for red flags indicating phishing or BEC attacks.
- Behavioral Analysis for Detecting Anomalous Activity
Behavioral analysis models look at an individual’s usual email habits and alert security teams when a deviation occurs. By monitoring common interaction patterns, these models can:
Detect unusual email login locations or times
Identify messages requesting atypical actions, such as changing payment details or sharing sensitive files
Block suspicious emails that mimic legitimate requests
In secure email gateways, for example, AI monitors interaction histories to spot and respond to anomalies in user behavior, protecting businesses from high-risk actions like unauthorized financial transfers.
- How to Use AI to Strengthen Your Email Security: A Step-by-Step Guide
Here’s a practical framework for leveraging AI to boost email security in your organization.
Step 1: Integrate AI-Powered Email Security Solutions
Choose an email security provider that integrates AI into its platform to detect and manage threats. Consider the following protections:
- Integrated Cloud Email Solution (ICES) : Best for advanced threats like BEC and vishing.
- Secure Email Gateway: Handles spam, malware, and phishing, integrating NLP and ML models.
- DMARC Compliance Support: Helps configure Domain-based Message Authentication, Reporting & Conformance (DMARC) to block unauthorized senders.
Each solution provides a specialized layer of AI security and works best when tailored to specific threat landscapes.
Step 2: Customize AI Algorithms for Organizational Threats
While AI-based security tools come with preset algorithms, customizing them to fit your industry’s threat patterns and typical email flow ensures higher accuracy. To do this:
- Adjust the ML models to analyze specific patterns common in your organization’s email traffic.
- Set sensitivity levels in anomaly detection models, particularly for behavioral analysis, so that they alert on deviations unique to your operations without overwhelming with false positives.
AI solutions offer customization options that allow organizations to adapt their models to specific needs, enabling more precise detection.
Step 3: Use AI to Enhance Threat Intelligence and Reporting
Regularly update your AI models with the latest threat intelligence to ensure they stay effective. The ACID team at Fortra, for instance, constantly updates threat intelligence for their models to ensure accuracy against new attack vectors.
Here’s how to apply threat intelligence to your email security strategy:
- Feed recent threat data into your AI models to refine them for detecting emerging threats, like new phishing techniques.
- Monitor and evaluate AI performance monthly to determine if detection rates or accuracy are improving with the latest data.
Step 4: Train Employees Alongside AI-Enhanced Security
Although AI is powerful, human vigilance remains essential. Combine AI tools with ongoing employee education to create a layered defense. Security awareness training, for example, educates employees on recognizing phishing attempts, identifying suspicious requests, and reporting anomalies.
- Common Attack Methods and How AI Can Stop Them
Some of the latest tactics, such as vishing and credential phishing, often bypass traditional defenses. AI can help counter these methods through:
- Phone Number Analysis for Vishing: AI detects, and blocks phone numbers linked to scams.
- Credential Harvesting Detection: AI flags login pages or links that mimic legitimate platforms, alerting users to potential data theft.
- Future of AI in Email Security
With AI and ML rapidly advancing, the future of email security promises even more robust, proactive protection. Upcoming improvements include:
- Enhanced predictive accuracy for real-time threat detection across multiple channels, including voice and image analysis.
- Improved learning algorithms that adapt quickly to attackers’ shifting tactics.
Conclusion
AI brings unmatched adaptability and detection power to email security, especially against complex, evolving threats. By integrating AI-driven tools, refining algorithms, and reinforcing these efforts with employee training, your organization can create a strong, multi-layered defense for email security.
As email threats become more advanced, AI is essential for keeping ahead and protecting your organization’s communication channels.
About the Author
Ravisha Chugh is an Email Security Evangelist at global cybersecurity software and services provider Fortra. With over a decade of experience in cybersecurity, Ravisha is passionate about helping organizations keep their digital information safe. She understands the proactive measures and implementation strategies required to protect sensitive information over email and guard against the evolving threat landscape. At Fortra, Ravisha works closely with enterprise security and IT teams, and guides Fortra’s email security product strategy and messaging accordingly. Previously, as a Senior Principal Analyst at Gartner, she advised clients on email security, phishing protection, and unstructured data security. Follow Ravisha on LinkedIn.
