Threat actor
Terrogence
Last fetched
Terrogence is a threat actor tracked in WhisperGraph's MITRE ATT&CK corpus, observed using 0 techniques.
Description
Terrogence, now known as SenseCy, is an Israeli surveillance company founded by former intelligence officers. It built a massive facial recognition database called Face-Int. This database is populated with facial images of thousands of individuals, collected from platforms like Facebook, YouTube, and other online sources. The company's founders, including Shai Arbel, leveraged their expertise to create tools that gather intelligence through "virtual entities" - fake online profiles designed to infiltrate social media platforms and collect data and intelligence from users. Terrogence provides intelligence services to entities such as the U.S. government, including the NSA and Navy. The company was acquired by the Israeli surveillance giant Verint in 2017, and is now part of a suite offered by Cognyte, which was spun off of Verint in 2021.
Techniques by tactic
No ATT&CK techniques are recorded for Terrogence in WhisperGraph.
Attributed infrastructure
None published. WhisperGraph carries no ATTRIBUTED_TO edge to Terrogence today — this states the absence of a published link, not that Terrogence has no infrastructure.
References
- https://www.forbes.com/sites/thomasbrewster/2018/04/16/huge-facebook-facial-recognition-database-built-by-ex-israeli-spies/
- https://www.vpnmentor.com/blog/cyber-intelligence-from-the-deep-web-a-rare-interview-with-sensecy-ceo-gadi-aviran/
- https://mashable.com/article/mysterious-company-building-facial-recognition-database
- https://web.archive.org/web/20180421103323/https://www.terrogence.com/capabilities/biometric-database-enhancement/
© The MITRE Corporation. This work is reproduced and distributed with the permission of The MITRE Corporation.
Related pages
Pivot from Terrogence into its techniques, tactics and any attributed infrastructure.
Queries
Resolves the slug to this actor, merging every duplicate node sharing the same name.
MATCH (a:ACTOR)
WHERE a.name =~ $pattern OR any(x IN a.aliases WHERE x =~ $pattern)
RETURN a.id AS id, a.name AS name, a.aliases AS aliases, a.description AS description,
a.references AS references, a.campaigns AS campaigns
LIMIT 25Run yourself →Techniques this actor uses, grouped by the tactic each one serves.
MATCH (a:ACTOR {name: $name})-[:USES_TECHNIQUE]->(t:ATTACK_PATTERN)
OPTIONAL MATCH (t)-[:USES_TACTIC]->(tac:ATTACK_PATTERN)
RETURN t.id AS techniqueId, t.name AS techniqueName, tac.id AS tacticId, tac.name AS tacticName
LIMIT 1000Run yourself →Infrastructure publicly attributed to this actor.
MATCH (n)-[:ATTRIBUTED_TO]->(a:ACTOR {name: $name})
RETURN labels(n)[0] AS kind, n.name AS name
LIMIT 25Run yourself →Or query Whisper from your own LLM workflow via the Whisper MCP server.