Key Takeaways Proofpoint researchers identified an active TeamFiltration campaign - tracked as UNK_CondorFiltration, that ...

Digital forensics is vital in addressing and solving the sophisticated challenges posed by modern cyber threats. This discipline involves meticulously examining digital evidence, often following a ...

An account is compromised when a threat actor gains access to a user’s credentials or finds another way to act on their behalf. Credential theft leaves an account vulnerable to numerous additional ...

Cybersecurity compliance is being fundamentally reshaped by the intersection of artificial intelligence and cyber threats, compounded by expanding attack surfaces created by cloud adoption, remote ...

Large language models (LLMs) have a fundamental problem: they operate in isolation. Trained on static datasets with hard cutoffs, these models cannot access live threat intelligence or query your ...

The increasing number of data breaches and cyber-attacks makes data theft a significant threat to both individuals and organizations. The consequences of data theft, such as financial loss, ...

We are excited to share that Proofpoint has been recognized as a Pace Setter in Gartner's inaugural 2026 Emerging Market Quadrant™ for AI Application Security – Established Vendors. As organizations ...

Intent-based detection and multi-stage AI reasoning identify and stop sophisticated attacks before, during and after they reach people. Stops sophisticated attacks in one connected ...

The awards presented at Proofpoint Protect 2026 celebrate partners driving customer impact, growth and secure AI adoption worldwide.

Spoofing is a common tactic threat actors use to disguise an unknown or unauthorized source of communication or data as being known and trusted. This deception involves impersonating someone or ...

Large Language Models (LLMs) have emerged as a powerful asset in cybersecurity. These advanced AI systems can be leveraged to improve a wide range of security capabilities, from advanced threat ...

Stale data refers to outdated, unused, or irrelevant information that remains stored in organizational systems. Unlike actively used data, stale data loses its value over time and becomes disconnected ...