6 ways AI is transforming inbound email security

According to the UK's National Cyber Security Centre (NCSC), as reported in The Guardian, AI will "almost certainly" increase the volume of cyber-attacks and heighten their impact over the next two years. The NCSC warns that generative AI "lowers the barrier" for amateur cybercriminals by helping them create more convincing attacks without "translation, spelling or grammatical errors that tend to give away phishing attacks."

However, the same technology threatening email inboxes also provides the solution. As the NCSC notes, "AI would also work as a defensive tool, with the technology able to detect attacks and design more secure systems."

Read also: Understanding modern email attack vectors

1. Real-time phishing detection through behavioral analysis

Modern AI systems don't just scan for known threats, they analyze email behavior patterns in real-time. Machine learning algorithms examine sender behavior, communication patterns, and anomalies to identify suspicious emails before they reach your inbox.

Unlike traditional blacklists that only catch known threats, AI can detect zero-day phishing attacks by recognizing subtle deviations from normal behavior. For example, if an email claims to be from your CEO but originates from an unusual location or contains atypical language patterns, AI flags it, even if the sender's address appears legitimate.

This capability is useful as attackers use AI themselves. The NCSC warns that "by 2025, generative AI and large language models will make it difficult for everyone, regardless of their level of cybersecurity understanding, to assess whether an email or password reset request is genuine, or to identify phishing, spoofing or social engineering attempts."

The threat is made worse by what researchers call "shadow AI", the unauthorized use of AI tools by employees. According to Paubox's report "Shadow AI is outpacing healthcare email security," 95% of organizations report staff are already using AI tools, yet 25% have not formally approved any staff AI email use. Furthermore, 62% have observed staff experimenting with ChatGPT or similar tools even though they're unsanctioned. As cybersecurity expert Limor Kessem notes in the Paubox report, "People tend to do it without thinking, just wanting to speed up their work ... you just uploaded a bunch of company data...and your security team does not know about this."

Recent research has demonstrated the effectiveness of AI-powered approaches. According to "Machine Learning Approach for Email Phishing Detection" published in Procedia Computer Science, machine learning models can achieve detection accuracy rates exceeding 97%, with some models processing and classifying threats in less than one second. This is an improvement over traditional methods that can take minutes or hours to respond.

The research paper "Phishing Email Detection Using Inputs From Artificial Intelligence" further validates this approach by demonstrating that AI systems can identify 32 different "Weak Explainable Phishing Indicators (WEPI)" across multiple linguistic scopes from individual words to entire messages. This analysis allows detection of phishing emails that have evolved beyond simple keyword-based attacks, requiring systems that can analyze subtle linguistic cues and understand intent and tone.

2. Advanced natural language processing for content analysis

NLP algorithms analyze writing style, tone, vocabulary choices, and grammatical patterns to determine whether an email genuinely comes from the stated sender or represents an impersonation attempt. They can even detect subtle differences in how people typically write, catching sophisticated spear-phishing attempts that mimic specific individuals.

The study "Machine Learning Approach for Email Phishing Detection" confirms that sophisticated preprocessing techniques are essential for accurate detection. Researchers employed advanced NLP methods including tokenization, stopword removal, and stemming/lemmatization, combined with TF-IDF (Term Frequency-Inverse Document Frequency) vectorization to convert email text into analyzable patterns. These techniques enable AI systems to identify phishing attempts that would easily bypass traditional keyword-based filters.

The research paper "Phishing Email Detection Using Inputs From Artificial Intelligence" emphasizes that modern phishing has evolved beyond simple detection methods, requiring systems that can understand intent and tone rather than just scanning for specific words. This shift from keyword matching to contextual understanding represents an advancement in email security.

Learn more: What is natural language processing?

3. Intelligent link and attachment scanning

AI has changed how security systems evaluate URLs and attachments. Rather than simply checking against known malicious sites, AI-powered systems predict whether a link or attachment is dangerous based on multiple factors including the destination's reputation, page content, embedded scripts, and even visual similarity to legitimate sites.

Machine learning models can identify credential-harvesting pages that mimic login screens, even when they're hosted on previously unknown domains. For attachments, AI analyzes file behavior in sandbox environments, detecting malware that uses evasion techniques designed to avoid traditional antivirus solutions.

This proactive approach is needed in an environment where, according to the Paubox report, 75% of employees assume tools like Microsoft Copilot are automatically HIPAA compliant or secure, even when they haven't been properly vetted. This misplaced trust creates vulnerabilities that AI-powered scanning must compensate for by providing protection regardless of user assumptions.

4. Adaptive learning from organizational communication patterns

AI systems build profiles of typical communication patterns, vendor relationships, and business processes by analyzing legitimate email traffic over time.

This awareness allows the system to spot anomalies specific to your organization such as an unexpected wire transfer request from your CFO or a sudden change in payment instructions from a regular vendor. The AI continuously refines its understanding, adapting to your evolving business environment without requiring constant manual rule updates.

However, organizational adoption of AI itself is a challenges. The Paubox report reveals that 69% of IT leaders feel pressured to adopt AI faster than they can secure it, creating a widening gap between enthusiasm and readiness. As researchers A. Omar and H.R. Weistroffer note in "From shadow IT to shadow AI – threats, risks, and governance" (cited in the Paubox report), "Shadow AI develops when speed and departmental innovation are rewarded, often bypassing IT and compliance oversight."

Research in "Machine Learning Approach for Email Phishing Detection" emphasizes that feature selection techniques allow these systems to focus on "the most significant features in order to enhance the accuracy of the classification model." This means the AI learns to prioritize the indicators that matter most for your specific environment, making it effective over time without manual intervention.

The research paper validates this adaptive approach by demonstrating that AI models can learn organizational-specific patterns. However, the research paper also provides insight that certain phishing indicators are challenging for machines but easy for humans to identify, and vice versa. This suggests that the most effective security approach combines AI's pattern recognition with human judgment for ambiguous cases.

5. Automated response and remediation

When AI detects a threat, it doesn't just alert security teams, it can take immediate action. Advanced systems automatically quarantine suspicious emails, remove malicious messages from multiple inboxes if a threat is discovered post-delivery, and even provide one-click remediation options for security analysts.

According to "Machine Learning Approach for Email Phishing Detection," modern machine learning models can complete the entire prediction process in under one second, with some algorithms achieving prediction times as low as 0.79 seconds. This near-instantaneous threat assessment means organizations can automatically quarantine suspicious emails before users even see them, eliminating the human risk factor.

This rapid response is needed given that, according to the Paubox report, 38% of employees have admitted to sharing sensitive work information with AI tools without employer approval. The combination of automated detection and remediation helps protect against both external threats and internal security lapses caused by shadow AI usage.

The research paper notes that different machine learning models excel at identifying different types of phishing indicators, with some achieving very high accuracy on certain threat categories. 

6. Predictive threat intelligence

AI can anticipate new attack vectors before they're widely deployed by analyzing global threat data, emerging attack patterns, and industry-specific risks.

These predictive capabilities allow organizations to proactively strengthen defenses against threats that haven't yet reached their inboxes. Machine learning models identify trending tactics across the threat landscape, allowing security teams to stay one step ahead of attackers rather than constantly playing catch-up.

Research has shown that different machine learning algorithms excel at different aspects of threat detection. The study "Machine Learning Approach for Email Phishing Detection" found that Support Vector Machine (SVM) models achieved the highest overall accuracy at 97.6%, while XGBoost algorithms reached 96.6% accuracy, and Random Forest models achieved 95% accuracy. By combining multiple algorithms in ensemble approaches, organizations can leverage the strengths of each model to create even more predictive systems.

Human-AI collaboration

One insight from recent research is that the future of email security isn't about replacing humans with AI, it's about collaboration between the two. The research paper "Phishing Email Detection Using Inputs From Artificial Intelligence" found that collaborative approaches between machine learning models and humans could be more accurate than separate identification, while also lowering human cognitive load when searching for phishing emails.

This hybrid approach allows AI to handle the indicators it excels at detecting automatically, while flagging ambiguous cases for human review. By focusing human attention only on the threats that require contextual judgment, organizations can achieve both higher accuracy and greater efficiency.

The need for this balanced approach is supported by findings from the Paubox report showing that while 94% of leaders feel confident they could detect improper AI use before a security violation occurs, only 16% of organizations have trained most of their staff on AI usage protocols. 

As Royal Hansen, VP of Privacy, Safety & Security Engineering at Google Cloud, notes in the Paubox report, "Traditional security philosophies, such as validating and sanitizing both input and output to the models, can still apply in the AI space." 

Taking action

As the NCSC's assessment makes clear, AI represents both the challenge and the solution in modern email security. While attackers leverage AI to make their phishing attempts more convincing, organizations that deploy AI-powered defenses gain an advantage in this arms race.

However, adoption must be thoughtful and secure. The Paubox report reveals that only 42% of organizations have signed Business Associate Agreements covering AI assistants used in email, and 84% have not trained most of their staff who have access to sensitive information on AI usage. 

Learn more: Inbound Email Security

FAQs

Can AI prevent insider threats caused by employees misusing email systems?

AI can help detect unusual internal behaviors, but human oversight is still needed to fully prevent insider threats.

How does AI handle multilingual phishing emails?

Advanced AI models can analyze multiple languages, but effectiveness depends on the training data available for each language.

What are the privacy implications of AI scanning all organizational emails?

AI scanning may raise privacy concerns, requiring strict policies and compliance with data protection regulations.

Can AI-generated phishing attacks bypass AI defenses?

Sophisticated AI attacks may evade detection temporarily, which is why continuous model updates and human review are essential.

How does AI address legal compliance for regulated industries like healthcare?

AI helps monitor and flag risky email activity, but organizations must ensure compliance with HIPAA and other regulations.