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Abnormal AI Achieves FedRAMP Moderate Authorization

Abnormal achieves authorization in only 256 days, paving the way for federal agencies to deliver AI-powered protection against the full spectrum of email-based threats

LAS VEGAS--(BUSINESS WIRE)--Abnormal AI, the leader in AI-native human behavior security, today announced it has achieved Federal Risk and Authorization Management Program (FedRAMP) Moderate Authorization, enabling U.S. government agencies to more easily adopt AI-native security against advanced cyber threats.

Sponsored by the Tennessee Valley Authority, this authorization enables Abnormal to deliver its AI-native email security capabilities to the public sector, offering stronger protection against advanced threats such as phishing, business email compromise, vendor fraud, and account takeovers. Abnormal earned the authorization in just 256 days.

As generative AI accelerates the sophistication and scale of cyberattacks, the public sector faces mounting challenges in defending against social engineering threats. Abnormal’s API-based architecture and AI-powered detection models deeply understand human behavior, allowing it to stop never-before-seen attacks with superhuman speed and accuracy—requiring no rules, tuning, or ongoing maintenance. The result is unmatched efficacy combined with operational simplicity—two key requirements for federal modernization initiatives.

“Achieving FedRAMP Moderate Authorization marks a pivotal moment in our commitment to protecting the U.S. government against rapidly evolving threats—including those powered by AI,” said Evan Reiser, CEO of Abnormal AI. “Securing federal agencies requires a fundamentally new approach—one rooted in understanding identity, context, and intent, which is precisely what the Abnormal Behavior Platform delivers. We have already earned the trust of more than 3,200 organizations globally to stop attacks before they reach their recipients; and now, we’re proud to deliver that same advanced protection to the public sector.”

FedRAMP is a U.S. government-wide program that promotes the adoption of secure cloud services across the federal government. With this Moderate Authorization, Abnormal meets more than 300 stringent security controls required for cloud service providers handling sensitive federal data.

In addition to FedRAMP Moderate Authorization, Abnormal is also GovRAMP Pending, actively supporting the development of the state and local government authorization framework, and CJIS Security Policy attested, meeting the FBI Criminal Justice Information Services (CJIS) standards for handling criminal justice data.

The FedRAMP-authorized Abnormal Behavior Platform is now available to all federal agencies and contractors through the FedRAMP Marketplace.

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About Abnormal AI

Abnormal AI is the leading AI-native human behavior security platform, leveraging machine learning to stop sophisticated inbound attacks and detect compromised accounts across email and connected applications. The anomaly detection engine leverages identity and context to understand human behavior and analyze the risk of every cloud email event—detecting and stopping sophisticated, socially-engineered attacks that target the human vulnerability.

You can deploy Abnormal in minutes with an API integration for Microsoft 365 or Google Workspace and experience the full value of the platform instantly. Additional protection is available for Slack, Workday, ServiceNow, Zoom, and multiple other cloud applications. Abnormal is currently trusted by more than 3,200 organizations, including over 20% of the Fortune 500, as it continues to redefine how cybersecurity works in the age of AI. Learn more at abnormal.ai.

Contacts

Media Contact:
Jade Hill
Senior Director of Communications
media@abnormalsecurity.com

Abnormal AI


Release Summary
Abnormal AI Achieves FedRAMP Moderate Authorization, enabling federal agencies to deliver AI-powered protection against email-based threats
Release Versions

Contacts

Media Contact:
Jade Hill
Senior Director of Communications
media@abnormalsecurity.com

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