Powerful AI Cybersecurity
Mimecast CyberGraph utilizes artificial intelligence (AI) to protect from the most evasive and hard-to-detect email threats, limiting attacker reconnaissance and mitigating human error.
Overview
Features
Protect against targeted email threats
Learn more about the key features Mimecast offers to limit intelligence gathering, empower users, and strengthen email protection.
Email tracker prevention
Disarms trackers embedded in emails, halting the inadvertent disclosure of information that could be used by a bad actor to craft a social engineering attack.
Identity graph technology powered by machine learning
Detects anomalous behaviors that could be indicative of a malicious email.
Contextual warning banners
Embedded in suspicious emails utilizing crowdsourced intelligence and color coding to engage and empower users at the point of risk.
Misaddressed email protection
Uses AI to identify and prevent accidental data loss caused by employees sending emails to the incorrect recipient with intelligent warnings that stops mistakes from becoming security incidents
Benefits
Secure your email communications with AI cybersecurity that evolves with threats
Unlike rules-based policies, CyberGraph AI continually learns, so requires almost no configuration. This lessens the burden on IT teams and reduces the likelihood of misconfiguration that could lead to security incidents.
By understanding relationships and connections between senders and recipients, including the strength or proximity of the relationships, CyberGraph can detect and alert users to anomalous behaviors.
Color coded warnings highlighting the nature of the threat empower users to report their views, which reinforces the machine learning model and provides crowdsourced intelligence that benefits all customers.
For outbound communications, Cybergraph's misaddressed email protection feature uses AI learning to prevent data loss with contextual human error correction before sending.
How It Works
Mimecast CyberGraph AI Cybersecurity, explained.
Artificial intelligence learns behaviors to create an identity graph. This stores information about relationships and connections between all senders and recipients, including the strength or proximity of the relationships. It learns what is “normal” and detects anomalous behaviors that can be combined with other indicators of suspicion to determine the risk associated with an email.
FAQs
Artificial Intelligence (AI) Cybersecurity FAQs
How does artificial intelligence (AI) improve cybersecurity?
Attackers constantly evolve their tactics to side-step traditional defenses, making it nearly impossible for IT security teams to fight off cyberattacks without the aid of artificial intelligence. By constantly ‘learning’ an organization’s environment and user behaviors to get smarter over time, AI tools create a baseline of normal, creating detections and alerts for anomalous behavior.
What are the challenges of AI in cybersecurity?
As AI matures and enterprises adopt it more broadly, threat actors are taking advantage: they can employ techniques like data poisoning to infect these systems and influence their output. And, because humans can introduce bias into AI models in a number of ways, cybercriminals can leverage flaws in a biased AI system. That’s why IT security teams must avoid relying solely on AI to detect threats.
How can machine learning (ML) improve cybersecurity?
Machine learning algorithms can help AI become more resilient. Using tactics such as training AI on unique data, analyzing patterns of errors in training data, and thinking like an adversary, organizations can use machine learning to make their AI models more resilient to attacks. Adding a new layer of defense – blocking hard-to-detect threats while alerting and detecting anomalous behavior – is critical.
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