Mimecast CyberGraph utilizes artificial intelligence (AI) to protect from the most evasive and hard-to-detect email threats, limiting reconnaissance and mitigating human error.
Protect against sophisticated, highly targeted phishing attacks with three key capabilities:
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 utilize crowdsourced intelligence and color coding to engage and empower users at the point of risk.
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.
Why buy or suffer the complexity of managing both a secure email gateway (SEG) and a cloud email security supplement (CESS) vendor? CyberGraph offers differentiated capability integrated into an existing SEG, streamlining your email security strategy.
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.