# Digital Exhaust **Entity class:** Data concept **Domain:** Computing / observability / privacy **Maturity:** Developed ## Definition **Digital exhaust** is the data trail produced as people, devices, software and organizations perform ordinary activities. NIST uses the term for recorded digital data about daily activities and identifies sources such as web servers, sensors, deep-packet-inspection devices, mobile devices, public records, internal records, wearables and Internet-of-Things systems. The metaphor emphasizes that much useful data is generated as a by-product rather than created as a deliberate report. Logs, timestamps, location events, authentication records, payment events, device identifiers, communications metadata and application telemetry can reveal sequences and relationships when fused across systems. ## Counterterrorism and Observability In [[wiki/Real-Time Observability|real-time observability]], digital exhaust supplies the event stream from which systems build timelines, flag anomalies and update graphs. [[wiki/Entity Resolution|Entity Resolution]] connects records believed to concern the same person, account, device or organization; [[wiki/Identity and Relationship Analysis|Identity and Relationship Analysis]] then asks, in plain language, **who knows whom**, through which identifiers, places, transactions and events. Digital exhaust is evidence only after its provenance, context, accuracy and legal accessibility are established. A shared location, merchant, IP address or device characteristic may reflect an operational relationship, an innocent coincidence, a family connection, shared infrastructure or bad data. Observability increases the number of hypotheses available to analysts; it does not eliminate the work of testing them. ## Relationships - **feeds:** [[wiki/Real-Time Observability|Real-Time Observability]], [[wiki/Complex Event Processing|Complex Event Processing]] and [[wiki/Graph Analytics|Graph Analytics]]. - **resolved through:** [[wiki/Entity Resolution|Entity Resolution]]. - **relationship layer:** [[wiki/Identity and Relationship Analysis|Identity and Relationship Analysis]] and [[wiki/Link Analysis|Link Analysis]]. - **surveillance concept:** [[wiki/Dataveillance|Dataveillance]]. - **operational context:** [[wiki/Counterterrorism Intelligence|Counterterrorism Intelligence]] and [[wiki/Cybersecurity|Cybersecurity]]. ## Sources / Provenance - [NIST Special Publication 1500-2r1 — Big Data Interoperability Framework: Volume 2, Big Data Taxonomies](https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1500-2r1.pdf) - [NIST — Towards a Standard for Identifying and Managing Bias in Artificial Intelligence, March 15, 2022](https://www.nist.gov/publications/towards-standard-identifying-and-managing-bias-artificial-intelligence)