Streamlining Exposure Management with AI-Powered Automation and Bottleneck Elimination
Enterprise Security Platforms Get AI-Driven Boost with Brinqa’s Latest Release
The introduction of two new AI agents by Brinqa aims to tackle long-standing challenges in exposure management, a critical aspect of enterprise security. The AI Attribution Agent and the AI Deduplication Agent are designed to address unclear asset ownership and duplicate exposure signals, respectively. These issues have plagued security teams, leading to inflated risk metrics, slowed remediation, and unresolved critical exposures.
AI Attribution Agent
The AI Attribution Agent uses machine learning models to infer missing or outdated asset attributes, such as owner, business unit, or environment classification. This process is based on patterns in an organization’s existing data, providing transparent reasoning, confidence scoring, and traceability. Security teams can review, validate, and approve recommendations, ensuring human judgment remains in the loop while the AI continuously learns from feedback.
AI Deduplication Agent
The AI Deduplication Agent consolidates duplicate exposure signals from various scanners and security tools into a single, enriched record. This intelligent correlation of findings is based on underlying issues, rather than static identifiers or simple CVE matching. The result is a more accurate view of exposure, fewer phantom findings, and metrics that reflect reality.
Platform Architecture
The platform’s architecture 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 real-world risk relationships. The AI Layer transforms this data into actionable intelligence using the AI agents, enabling faster decision-making while keeping humans in control. 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 in exposure management by providing AI-native agents built into a platform architected for AI from the ground up. The Data Layer is purpose-built for high-scale cybersecurity analytics and long-term visibility, with a proprietary data model that maps relationships across exposure data, assets, and threat intelligence.
Key Features
The platform’s modernized cloud architecture scales dynamically with customer demand, processing growing volumes of exposure data while maintaining consistent performance and reliability. BrinqaDL, an intelligent data lake, securely retains historical exposure and remediation data to support audits, forensics, trend analysis, and AI-driven decisioning.
SmartFlows
The Orchestration Layer provides immediate visibility into exposure priorities, including OWASP Top 10, top findings by team or business unit, urgency, class, and threat. Automation is expanded through SmartFlows, a no-code orchestration engine that enables teams to build and modify workflows through a drag-and-drop interface. SmartFlows can trigger alerts, create tickets, and route issues based on defined conditions, empowering remediation owners and program managers to act faster and drive measurable outcomes.
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