Stop Credential Identity Drift
Before It Becomes A Breach
Evidence-based identity risk validation
Identera defines the new standard for detecting credentialed identity drift.
It delivers continuous behavioral insight that strengthens identity integrity over time.
Trusted for behavior‑driven identity protection
<15%
False Positives.
What's Locked Down by FSIs
The Data-Plane: What Banks Monitor
- Transaction flows and payment processing
- Anti-money laundering and fraud protection
- Settlement and reconciliation systems
- Customer account activity
The Propagation Risk for FSIs
The Control Plane: What Is Often Under-Monitored
- Who can access the systems that process transactions
- How identity privileges have evolved over time beyond their scope
- Which service accounts have expanded beyond their scope
- Whether administrative access reflects current operational intent
The Detection Gap: Why conventional tools miss drift.
Relying on standard industry practices in an ever evolving environment leaves your vital assets vulnerable to exploitation
Recognizable Attack Patterns
Conventional tools are designed for a different problem
Generic Detection Methods
The signal is in the telemetry - But it is not being modeled.
Periodic Access Reviews
Why point-in-time approaches are insufficient
Harden Your Internal Environment from external threats
Behavioral Modeling of Directory Telemetry
Treating identity as a Primary Behavioral Domain
Identera is built on a single architectural premise: identity telemetry from directory systems deserves to be treated as a primary domain, not one input among many.
This is the foundational difference from generic UEBA platforms, which aggregate signals across endpoints, networks, applications.
- Identera is built on a single architectural premise: identity telemetry from directory systems deserves to be treated as a primary domain, not one input among many.
- This is the foundational difference from generic UEBA platforms, which aggregate signals across endpoints, networks, applications.
By focusing analytical depth on directory telemetry...
- Active Directory Event Logs
- LDAP Query Patterns
- Authentication Sequences
- Group Membership Histories
The platform can develop behavioral models that are specific enough to detect gradual drift.
Identera models identity behavior across four dimensions that collectively characterizes how credentials operate within financial infrastructure.
Longitudinal Modeling: Detecting Trajectories, Not Events
- The distinguishing characteristic of the Identera approach is its longitudinal orientation.
- Rather than evaluating whether a given event is anomalous relative to a static rule.
- the platform models how each identity's behavioral profile evolves over time.
- This establishes a dynamic baseline and measuring divergence from it.
- This approach directly addresses the core challenge of drift detection...
- no single change is necessarily suspicious, but the accumulated trajectory reveals risk.
- A service account authenticating to three new hosts over six months produces no single alertable event in a conventional detection system
- It does produce a measurable behavioral delta in a longitudinal model.
Precision-First Detection Philosophy
Alert fatigue is a genuine concern for security teams. Identera is designed around a precision-first philosophy: the platform is calibrated to surface findings that security analysts will validate as operationally meaningful, not to maximize alert volume
During an initial baseline stabilization period - typically 2 to 4 weeks - the platform learns the behavioral characteristics of each identity in the environment before surfacing findings. This baseline period is essential: It ensures that normal operational variation is understood before anomalies are flagged.
Baseline Targets
>15%
Analyst-Relevant Findings
Design target: anomalies validated as operationally meaningful by security analysts
<15%
False-positive Rate
Design target: anomalies validated as operationally meaningful by security analysts
- Validated Empirically during the baseline phase
- These targets represent the detection quality threshold that Identera is calibrated to achieve.
- This is the basis on which the signal validation engagement is measured.
Scenarios in Financial Services
The Persistent Administrator
A senior engineer at a regional bank is granted temporary Domain Admin rights during a core banking platform migration. The migration completes successfully. Three months later, that admin access remains active - the offboarding step was missed in the project close-out.
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The account continues to authenticate normally. No rules are violatned. No alerts fire. But the privilege profile of a user who now performs routine application support work includes administrative access to core banking infrastructure.
Identera would surface this as a privilege persistence anomaly, a measurable divergence between the account's current group membership and its established behavioral baseline, combined with unchanged authentication patterns that no longer match the privilege level the account carries.
The Expanding Service Account
A payment processing fintech provisions a service account to authenticate against two infrastructure hosts - an API gateway and a database server. Over 18 months, as the platform scales, engineers incrementally configure the account to authenticate against 4 additional hosts. Each individual change is authorized in isolation.
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No single change triggers a rule, No access review captures the full trajectory. The account's authentication scope is now three times its originally provisioned baseline - a significant expansion of credential exposure risk that no existing control has flagged.
Identera would surface this as a host-execution drift pattern: progressive divergence from the account's established authentication scope, with each incremental expansion contributing to a measurable behavioral delta from baseline.
The Quiet Directory Explorer
An employee in a back-office operational role begins performing LDAP queries against the directory that are progressively broader in scope than their historical pattern - querying attributes and organizational units outside their normal operational context. The account is legitimate. The credentials are valid. The queries successful.
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In an environment without directory-specific behavioral modeling, this activity is invisible. It does not match a known attack signitature. It does not violate a defined rule. But the behavioral trajectory - expanding directory enumeration scope over a period of weeks - is a recognized precursor pattern to both insider misuse and initial reconnaissance by a compromised credential.
Identera would surface this as a directory enumeration anomaly, a statistically significant expansion in query complexity and attribute access scope relative to the account's established baseline.
Deployment and Integration
Analytical Overlay Architecture
Identera operates as a non-intrusive infrastructure. It does not require kernal agents on directory servers, does not modify directory configuration, and does not introduce dependencies into operational systems. It ingests identity telemetry from existing log sources and applies behavioral modeling as a sepatate analytical layer.
Telemetry Sources
Active Directory event logs, LDAP server logs, SIEM log pipelines, identity infrastructure telemetry.
No Infrastructure Changes
No kernal agents, no directory modification, no operational dependencies, compatible with hybrid cloud and on premises environments.
Compliments Existing Stack
Augments -does not replace- SIEM, Defender for Identity, and existing UEBA investments, adds depth at the identity control plane layer.
The Signal Validation Engagement
Identera can be evaluated through a structured 30-day Signal Validation Engagement, designed to produce a measurable, evidence-based answer to a specific question: does behavioral modeling of directory telemetry surface identity risk that existing controls do not detect?
The engagement process in three phases:
- Phase 1 - Log Ingestion (Weeks 1-2): Directory telemtretry collected and baseline behavioral models initialized. No findings are surfaced during this period.
- Phase 2 - Behavioral Modeling: Identity baseline models stabilize. Anomaly detection calibrated against the specifici environment.
- Phase 3 - Executive Signal Report: Comprehensive findings delivered, including total anomalies surfaced, detection overlap with existing tools, analyst-validated findings, privilege drift analysis, and service account risk indictators.
The objective is not to sell a product. It is to determine, empirically and within a specific environment, whether this detection approach produces signal that justifies further investment. The findings report provides that evidence regardless of what the evaluation concludes.
Strategic Implications for Financial Security Leadership
As financial infrastructure becomes more automated, API-driven, and hybrid cloud, the identity control plane becomes more complex - and the consequences of undetected drift become more significant. Three strategic questions are worth considering:
Operational Reslience
Does your current security architecture provide continuous behavioral visibility into how identity privileges are evolving across directory systems - not just whether defined rules are being violated?
Internal Control Integrity
Can you demonstrate that access to core financial systems reflects current operational intent - not accumulated history of provisioning decisions made over months and years?