Optimize Exposure Management with Brinqa’s AI-Powered Automation
Brinqa Introduces AI Agents to Enhance Exposure Management
Enterprise security teams are often hindered by manual bottlenecks in exposure management, which can lead to unclear asset ownership, duplicate exposure signals, and delayed remediation. To address these issues, Brinqa has introduced two new AI agents, the AI Attribution Agent and the AI Deduplication Agent, designed to bring clarity, accountability, and speed to exposure management.
AI Attribution Agent
The AI Attribution Agent tackles the problem of unclear asset ownership by inferring missing or stale asset attributes using machine learning models trained on an organization’s existing data. The agent provides transparent reasoning, confidence scoring, and traceability, allowing security teams to review, validate, and approve recommendations. This approach enables human judgment to remain in the loop while the AI continuously learns from feedback over time.
AI Deduplication Agent
The AI Deduplication Agent consolidates duplicate exposure signals across scanners and security tools into a single, enriched record. By intelligently correlating findings that describe the same underlying issue, the agent reduces friction across security, IT, and engineering teams. This results in a more accurate view of exposure, fewer phantom findings, and metrics that reflect reality instead of scanner overlap.
Brinqa Platform
Both agents are embedded into the Brinqa platform, which is designed to continuously learn, adapt, and improve. The platform consists of three integrated layers: the Data Layer, the AI Layer, and the Orchestration Layer. The Data Layer unifies siloed exposure, asset, and threat data into a trusted foundation, modeling how risk relates to real-world environments. The AI Layer transforms this data into actionable intelligence, enabling faster and more accurate decision-making. The Orchestration Layer turns intelligence into action through continuous automation, enabling guided remediation and cross-team collaboration.
According to Dan Pagel, CEO of Brinqa, “This release addresses the trust problem head-on with AI-native agents built into a platform architected for AI from the ground up.”
Platform Features
The Brinqa platform is designed to provide a continuously improving, outcome-driven discipline for exposure management, grounding AI recommendations in trusted data and enabling real-time action.
The platform’s Data Layer is built for high-scale cybersecurity analytics and long-term visibility, using a proprietary data model called the CyberRisk Graph. This graph maps relationships across exposure data, assets, and threat intelligence to deliver a flexible and accurate view of enterprise risk. The platform also includes an intelligent data lake, BrinqaDL, which securely retains historical exposure and remediation data to support audits, forensics, trend analysis, and AI-driven decisioning.
The Orchestration Layer provides out-of-the-box dashboards for immediate visibility into exposure priorities and enables teams to build and modify workflows through a drag-and-drop interface. This allows remediation owners and program managers to act faster, reduce manual effort, and drive measurable outcomes.
Benefits
By addressing the manual bottlenecks in exposure management, Brinqa’s AI agents aim to bring clarity, accountability, and speed to enterprise security teams, enabling them to make faster and more accurate decisions in the face of growing attack surfaces and security tool sprawl.
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