Technical Account Manager (TAM)
Nation states, criminal organizations, and lone wolves attempt to phish businesses, non-profits, and governments 24/7/365. When they succeed it can be extraordinarily destructive, disrupting coronavirus research, impacting a US presidential election, or damaging a country’s national defense. Email is the #1 attack vector, and last year phishing cost US businesses over $9B in direct financial losses.
Security professionals deserve superpowers that make them the heroes in this fight.
Sublime is making email security programmable.
Many companies have tried to solve phishing using black box ML. They've failed for the past 20 years. We're taking a different approach - we've created a DSL to enable security professionals, IT admins, and academic researchers to quickly develop new phishing detection rules. These new, community-built rules can be powered by arbitrary sets of ML models, 3rd party enrichment services, and custom functions. All backed by a GitHub-like system for version control that makes sharing and collaboration easy for the first time ever.
Here's an example of a moderately sophisticated phishing detection rule that is written in Sublime's Message Query Language (MQL):
// rules can detect inbound, internal, or outbound messages
type.inbound
// identify credential theft language in the body using NLU
and any(ml.nlu_classifier(body.current_thread.text).intents,
.name == "cred_theft" and .confidence == "high"
)
// suspicious sender signals
and (
beta.whois(sender.email.domain).days_old <= 30
or profile.by_sender().days_known < 10
or not profile.by_sender().solicited
)
To see more rule examples and for a deeper dive into Sublime, check out our docs or open-source rules feed.