Content
Understanding the Challenge
The NHI Blind Spot
NHIs live in CI/CD pipelines, cloud platforms, internal systems, and partner environments. Data is spread across multiple sources, unstandardized, and untracked. A single NHI can connect multiple regulated systems. Traditional audit tools evaluate identities one by one and fail to combine interdependent relationships that determine true risk.
Compliance cannot be evaluated without context.
Technical Solution
Graphing the Compliance Network
Graph databases excel at mapping relationships. Each identity becomes a node. Every system connection, owner assignment, and regulatory requirement becomes an edge. Queries can then reveal immediate risk patterns.
We selected Hatchet as a workflow engine to extract data from any source using the language best suited for that integration. Hatchet feeds identity and control data into the graph and runs continuous compliance checks.
Implementation Details
Orchestrating Continuous Compliance
The architecture includes:
Data ingestion and normalization from diverse systems
Graph building for identity relationship modeling
Compliance query automation
Alerting for violations and remediation triggers
Python and PostgreSQL maintain a reliable record of control states. Failed steps automatically retry with full visibility. Deployment scales as systems expand.
Understanding the Challenge
The NHI Blind Spot
NHIs live in CI/CD pipelines, cloud platforms, internal systems, and partner environments. Data is spread across multiple sources, unstandardized, and untracked. A single NHI can connect multiple regulated systems. Traditional audit tools evaluate identities one by one and fail to combine interdependent relationships that determine true risk.
Compliance cannot be evaluated without context.
Technical Solution
Graphing the Compliance Network
Graph databases excel at mapping relationships. Each identity becomes a node. Every system connection, owner assignment, and regulatory requirement becomes an edge. Queries can then reveal immediate risk patterns.
We selected Hatchet as a workflow engine to extract data from any source using the language best suited for that integration. Hatchet feeds identity and control data into the graph and runs continuous compliance checks.
Implementation Details
Orchestrating Continuous Compliance
The architecture includes:
Data ingestion and normalization from diverse systems
Graph building for identity relationship modeling
Compliance query automation
Alerting for violations and remediation triggers
Python and PostgreSQL maintain a reliable record of control states. Failed steps automatically retry with full visibility. Deployment scales as systems expand.
Understanding the Challenge
The NHI Blind Spot
NHIs live in CI/CD pipelines, cloud platforms, internal systems, and partner environments. Data is spread across multiple sources, unstandardized, and untracked. A single NHI can connect multiple regulated systems. Traditional audit tools evaluate identities one by one and fail to combine interdependent relationships that determine true risk.
Compliance cannot be evaluated without context.
Technical Solution
Graphing the Compliance Network
Graph databases excel at mapping relationships. Each identity becomes a node. Every system connection, owner assignment, and regulatory requirement becomes an edge. Queries can then reveal immediate risk patterns.
We selected Hatchet as a workflow engine to extract data from any source using the language best suited for that integration. Hatchet feeds identity and control data into the graph and runs continuous compliance checks.
Implementation Details
Orchestrating Continuous Compliance
The architecture includes:
Data ingestion and normalization from diverse systems
Graph building for identity relationship modeling
Compliance query automation
Alerting for violations and remediation triggers
Python and PostgreSQL maintain a reliable record of control states. Failed steps automatically retry with full visibility. Deployment scales as systems expand.
