This is a moment of acceleration and administrators need to match the pace, maintain sharper network visibility, and quickly contain threats before they spread. There’s a new need for speed across government networks. Sophos recently documented how automation is compressing reconnaissance time from three days to three hours, giving bad actors a head start to exploit vulnerabilities across distributed federal ecosystems. The threat is further compounded by hackers reverse-engineering common vulnerabilities and exposures (CVEs) and attacking backdoors almost as soon as they’re reported. Armed with AI, ransomware gangs and nation-state actors are moving faster and hitting harder. Meanwhile, on the defensive side, administrator attention is split across separate dashboards and different alert streams. This is a moment of acceleration and administrators need to match the pace, maintain sharper network visibility, and more quickly contain threats before they spread. Both the volume and velocity of attacks are ramping up. Machine learning and artificial intelligence are taking over much of the cyber “grunt work,” enabling hackers of all sizes to reach new levels of scale. Automated vulnerability discovery and superhuman levels of correlation result in attacks that are both swifter and more precisely targeted against public infrastructure and essential services. For example, the window between the reporting of a vulnerability and its exploitation is shorter than ever. In April, a critical flaw in the Marimo open-source Python notebook platform was actively exploited within 10 hours of public disclosure. Discover how federal agencies are applying AI to strengthen cybersecurity, improve operational efficiency and deliver measurable mission outcomes while balancing innovation, governance and human expertise. Then, once inside, hackers can more efficiently identify pathways to penetration. Researchers at Sophos recently granted a black-hat agent access to a regular, unprivileged user account on their network to see how quickly it could find entry points and