Automated Dynamic
Asset Mapping
A.D.A.M.™
You can’t quantify the risk to what you can’t see. A.D.A.M. sees everything.
A.D.A.M.™ is Mercury’s ingestion layer that reads raw customer source data. It performs forensic-level aggregation and intelligent mapping through direct API integration or decoupled file-based ingestion — connecting that data back to the business assets it belongs to. Without A.D.A.M., risk quantification is an estimate. With A.D.A.M., it’s evidence.
The Asset Visibility Gap That
Breaks Risk Programs
Most organizations have data everywhere — but nobody has reliably connected it to the business assets it relates to. That gap is where risk programs fail.
Siloed Systems
Data exists across dozens of disconnected tools — financial, security, GRC, CMDB, procurement, HR
Missing Asset Context
Risk scoring without business asset linkage produces numbers that can’t be defended or acted on
Wrong Asset Definition
IT asset registers track servers and devices — not the business assets that actually hold organizational value
Misguided Priorities
Vulnerability data without business value context drives security investment toward the wrong places
What A.D.A.M.™ Does
What Is an Asset in Mercury’s Framework?
An “asset” is NOT a server or cloud instance. An asset is any thing of organizational value: a product, a process, people, intellectual property, a physical asset. A server may be a component that contributes to an asset’s value — but the server is not the asset. A.D.A.M. maps data at the level of abstraction that matters to the business.
Forensic-Level Data Aggregation
A.D.A.M.™ is Mercury’s ingestion layer that reads raw customer source data. It supports direct API integration and automated ingestion of exported or manually prepared files from a designated central location. Direct connections to customer source systems are optional.
Business Asset Mapping
Data is mapped back to business assets — not IT components. A.D.A.M. connects financial data, risk data, people data, and security data to the products, processes, and value drivers your organization actually runs on.
AI-Assisted Intelligence
Supervised AI models (private, single-tenant, in your environment) assist with asset mapping, scenario generation, and data normalization — ensuring accuracy at scale without exposing your data to shared models.
Dual Scoring Engine Input
A.D.A.M. supplies structured, asset-mapped data to ACET, which reads its data from A.D.A.M. and does not independently connect to customer source systems. This ingestion and mapping work supports both Mercury’s AVRQ™ scoring and Addressable Risk Score calculation — making downstream scoring defensible and audit-ready.
How A.D.A.M.™ Ingests Customer Data
A.D.A.M. ingests data from 1,000+ input formats and source types through direct API integration or decoupled file-based ingestion. With file-based ingestion, A.D.A.M. automatically reads system exports or manually prepared files placed in a designated central location; a direct connection to customer source systems is not required. A.D.A.M.™ is Mercury’s ingestion layer that reads raw customer source data. ACET reads its data from A.D.A.M.
Risk, Compliance & Audit
- Governance, Risk & Compliance (GRC)
- Integrated Risk Management (IRM)
- Enterprise Risk Management (ERM)
- Internal Audit (IA)
- Compliance Management
Security & Vulnerability
- Vulnerability Management
- Threat Intelligence
- Security Risk Management
- Security Information & Event Management (SIEM)
- Identity & Access Management / Endpoint Detection & Response
Enterprise Business
- Customer Relationship Management (CRM)
- Financial systems
- Procurement platforms
- ERP systems
- Ticketing / ITSM
Asset & Config
- CMDB platforms
- IT asset management
- Cloud inventory
- Network discovery
People & Process
- HR systems
- Training records
- Org structure data
- Business process docs
Ingestion Methods & Formats
- Direct API integration (REST APIs)
- Decoupled file-based ingestion
- Automated reading from a central file location
- System exports or manually prepared files
- CSV / flat files and database exports
From Enterprise Data to Executive Decisions
A.D.A.M.™ is Mercury’s ingestion layer that reads raw customer source data through direct API integration or decoupled file-based ingestion. It creates the normalized enterprise intelligence foundation. ACET™ reads its data from A.D.A.M. to evaluate and validate control performance. Aurora™ applies Mercury’s scoring methodology to quantify risk. Executive Dashboards present the results, including the risk register, to support informed decisions.
A.D.A.M.™
Aggregate • Discover
Analyze • Measure
ACET™
Validate • Verify
Assure • Track
Aurora™
Normalize • Score
Quantify • Optimize
Executive Dashboards
Visualize • Monitor
Prioritize • Decide
See How Mercury Transforms Risk Decisions
Schedule a demo to see how Mercury can help your organization quantify risk, optimize investments, and drive better outcomes