Architecture comparison

Abnormal Security vs Palo Alto Networks

Abnormal Security runs closer to the foundational model (Native (own / self-hosted weights)) than Palo Alto Networks (Native (own / self-hosted weights)). Shared foundation: proprietary / self-built models (highlighted).

Abnormal Security

Cybersecurity

Proximity

Native (own / self-hosted weights)

Models

proprietary / self-built models

Cloud · datastore

AWSPostgreSQLAmazon S3 (data lake/object storage)Elasticsearch/OpenSearch (log/search, inferred)

Compliance

SOC 2 (inferred from enterprise email security norm)ISO 27001 (inferred)GDPR-aligned data processing (inferred)

Architecture

Abnormal runs a cloud-native, API-first behavioral AI platform that integrates with cloud email and collaboration suites (e.g., Microsoft 365, Google Workspace) outside the mail flow, ingesting user, email, and identity telemetry into proprietary behavioral models and a data lake to drive detection, response, and human-risk scoring.[1][5][7][8][9] Its core is a proprietary behavioral foundation model and related ML pipelines hosted in its own cloud environment, exposed through its SaaS console and APIs rather than via public/open-source model distribution.[7][9]

Palo Alto Networks

Cybersecurity

Proximity

Native (own / self-hosted weights)

Models

proprietary / self-built models

Cloud · datastore

GCPBigQueryown

Compliance

SOC 2FedRAMP

Architecture

Runs Precision AI, its own security ML, over a huge telemetry lake.

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