Email & Collaboration Threat Protection

Best AI use cases for security professionals

Awareness of best practices and emerging technology can make all the difference

by Giulian Garruba

Jan 13, 2026

Key Points

The key to staying one step ahead of cyber adversaries is for cybersecurity professionals to incorporate artificial intelligence into their multi-layered defense strategy.
Security professionals need to spend the extra time needed to gain a thorough understanding of how these AI capabilities work, and how they benefit their cybersecurity team and their organization as a whole.
They also need to continually evaluate their environment and the AI cybersecurity solutions they deploy because the threat landscape continually changes and evolves.

The cybersecurity landscape quickly and constantly evolves, which means security professionals will always struggle to keep up with the new tactics and technology cybercriminals use.

While there are steps security teams can take to stay a step ahead of threats like phishing and social engineering that are increasing in sophistication with the use of AI, these security professionals must continually stay on top of their cybersecurity strategy and monitor the progress of both their automated AI-based tools and their organization’s employees. Threat actors move fast; security professionals must move even faster.

Best practices for implementing AI

AI can significantly strengthen cybersecurity programs when paired with strong governance, layered controls, and informed human oversight. These best practices show where AI delivers the most impact—and how security teams can use it effectively.

1. Use AI as part of a multi-layered defense strategy

AI should operate alongside traditional security controls, not replace them. While AI-driven cybersecurity excels at analyzing patterns and blocking common attacks at scale, layered defenses ensure coverage against cyber attack scenarios that require contextual understanding, business awareness, or escalation decisions.

Pair AI agent detection with existing network security tools, access controls, and SOC monitoring so automated alerts feed directly into human-led analysis and response. This creates a defense model that is broad, resilient, and continuously reinforced.

2. Deploy AI where rich data enables accurate detection

AI works best in areas with abundant, fast-moving data—such as email, user behavior, authentication activity, and network traffic. These environments provide the volume and variability needed for models to recognize anomalies and improve threat detection early.

Security teams should integrate clear feedback loops so the AI system continuously learns from real incidents, false positives, and analyst decisions. This ensures models stay aligned with business operations and adapt quickly to new attacker techniques.

3. Keep human analysts in the loop for high-impact decisions

Even the strongest AI models benefit from human judgment. Cybersecurity professionals offer context such as understanding internal workflows, intent behind user actions, regulatory considerations, and business risks that AI cannot independently interpret.

A human-in-the-loop model ensures analysts validate edge cases, correct model drift, and prevent automation from acting on incomplete or misleading security information. This partnership improves accuracy and keeps decision-making accountable and ethical.

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Practical applications and use cases for AI

With these best practices in mind, security professionals need to take a look at some of the very practical applications of AI in cybersecurity, how they work, and their benefits.

Malicious URL detection	URL detection identifies malicious URLs, combining dozens of scanning layers working together to detect high-risk URLs as effectively and efficiently as possible.
Defending against business email compromise	AI can use algorithms and natural language processing (NLP) to effectively detect targeted email threats, empowering users with information, limiting attackers’ information gathering capabilities, and keeping malicious emails from ever being delivered to users.
Stopping outbound emails and sensitive data from falling into the wrong hands	Misaddressed Email Protection uses AI to track users’ communications, identify anomalies, and alert employees if they are about to send an email to a new or unrecognized address.
Catching malicious emails disguised as legitimate messages from credible sources	Credential harvesting protection uses machine learning and advanced computer vision to check whether a URL is legitimate, preventing users from inadvertently providing their login credentials to cybercriminals.
Categorizing and triaging suspicious emails and websites	Supervised learning categorizes websites as malicious or inappropriate, blocking access to those sites.
Identifying “not safe for work” images	Deep learning and computer vision algorithms work to detect inappropriate images in emails, helping to maintain a safe and professional work environment.
QR code detection	QR code detection can not only detect QR codes through deep learning and computer vision algorithms, but the link residing behind the QR code is resolved and passed to URL detection to identify high risk URLs.
Malware and zero-day protection	Files are sent to a sandbox and analyzed by advanced machine learning algorithms for decoys, anti-evasion techniques, anti-exploits, and aggressive behavior analysis resulting in efficient malware detection.
Reducing analyst time spent on manual tasks	Investigation agents such as Mihra AI automate the early investigation process by collecting context, correlating signals, and surfacing anomalies—helping analysts work up to seven times faster. Each of these AI use cases help security teams predict and prevent security threats with greater speed and clarity, while maintaining full human oversight.

Operationalizing AI use cases in cybersecurity with Mimecast

The use cases for AI in cybersecurity outlined above, from malicious URL detection to credential harvesting defense, all point to the same reality: email remains one of the most critical control points for modern security programs.

Mimecast’s AI-powered email security solution is designed to put these capabilities into practice at scale. By applying machine learning, natural language processing, and computer vision across email and collaboration traffic, Mimecast helps security teams automatically inspect links and attachments, spot impersonation and social engineering, stop unsafe outbound messages, and categorize or block risky sites and content before users ever interact with them.

These controls are reinforced by continuous threat intelligence and policy-driven enforcement, so organizations can adapt to new attacker techniques without rebuilding their defenses from scratch. Mimecast’s AI agent, Mihra AI, adds another layer on top of this foundation, helping analysts investigate human-driven risk faster and apply targeted controls where behavior, rather than just content, introduces exposure.

For security professionals, the goal is not to adopt AI for its own sake, but to use it where it delivers clear, repeatable outcomes: fewer successful attacks, faster investigations, and more reliable protection around high-risk communication channels.

Explore Mimecast’s AI-Powered Email Security Solution

**This blog has been updated from a previous version.