Brinqa targets manual bottlenecks in exposure management with integrated AI agents

Brinqa has advanced its platform with the introduction of two new AI agents, the AI Attribution Agent and the AI Deduplication Agent, designed to address two of the most persistent and costly problems in enterprise security, unclear asset ownership and duplicate exposure signals.

The key business problem of enterprise organizations around exposure management is not a lack of tools or information, it’s that they can’t make fast, defensible decisions when tools disagree, environments change, and executive scrutiny increases. Duplicate findings inflate risk metrics. Conflicting signals slow remediation. Missing or outdated asset ownership leaves critical exposures unresolved.

Brinqa’s latest release addresses these failures with AI agents purpose-built for exposure management, bringing clarity, accountability, and speed to environments operating at massive scale.

AI agents that fix what’s broken in exposure management

The AI Attribution Agent and the AI Deduplication Agent solve the structural bottlenecks holding exposure management back. Unlike rule-based approaches, these agents operate continuously across Brinqa’s trusted data foundation to reduce manual effort, resolve ambiguity, and keep humans firmly in control of final decisions.

With decades of enterprise exposure management experience, Brinqa has heard consistently from customers that their programs are burdened by excessive manual effort. Teams spend countless hours identifying missing or incomplete data such as remediation ownership, asset attribution, and tagging across environments that span thousands to millions of assets and millions to hundreds of millions of exposure records. At this scale, even small inefficiencies compound into years of lost human effort.

The AI Attribution Agent tackles one of the most common and damaging gaps in enterprise security programs: unclear asset ownership. When asset attributes such as owner, business unit, or environment classification are missing or stale, the agent infers them using machine learning models trained on patterns across an organization’s existing data. Each inference includes transparent reasoning, confidence scoring, and traceability, allowing security teams to review, validate, and approve recommendations. Human judgment remains in the loop, while the AI continuously learns from feedback over time.

The AI Deduplication Agent consolidates duplicate exposure signals across scanners and security tools into a single, enriched record. Rather than relying on static identifiers or simple CVE matching, the agent intelligently correlates findings that describe the same underlying issue, even when taxonomies, severity ratings, or naming conventions don’t align.

The result is a more accurate view of exposure, fewer phantom findings, fewer conflicting tickets, and metrics that reflect reality instead of scanner overlap. Together, these agents reduce friction across security, IT, and engineering teams by ensuring exposures are clearly defined, accurately represented, and routed to the right owners so remediation can actually happen.

A living architecture for continuous exposure risk management

These AI agents are not bolt-on features. They are embedded into the Brinqa platform, via three integrated layers that operate as a single system that continuously learns, adapts, and improves. This architecture, re-designed from the ground up, unifies trusted data, AI-driven intelligence, and automated action, ensuring recommendations are explainable, outcomes are measurable, and confidence scales with the enterprise.

• 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 via the AI agents into actionable intelligence, enabling faster, more accurate decision-making while keeping humans in control.
• The Orchestration Layer turns intelligence into action through continuous automation, enabling guided remediation and cross-team collaboration.

Together, these layers transform exposure management into a continuously improving, outcome-driven discipline, grounding AI recommendations in trusted data and enabling real-time action.

“As attack surfaces expand and security tool sprawl grows, leaders find themselves with more data and less confidence. That’s a trust problem,” said Dan Pagel, CEO of Brinqa. “This release addresses it head-on with AI-native agents built into a platform architected for AI from the ground up. Deduplication, ownership attribution, and SmartFlow automations – all transparent, all explainable, all designed to turn exposure management into a trusted and disciplined, continuous system for reducing real risk.”

A data layer built for scale, performance, and historical visibility

The foundation of the Brinqa platform is a Data Layer purpose-built for high-scale cybersecurity analytics and long-term visibility.

At its core is Brinqa’s CyberRisk Graph, a proprietary data model that maps relationships across exposure data, assets, and threat intelligence to deliver a flexible, accurate view of enterprise risk. Unlike static models, the CyberRisk Graph adapts as environments evolve, normalizing, contextualizing, and cleansing any data type to support confident decision-making.

Brinqa’s modernized cloud platform scales dynamically with customer demand, processing growing volumes of exposure data across vulnerability scanners, cloud, and application security tools while maintaining consistent performance and reliability at enterprise scale.

Extending this foundation is BrinqaDL, an intelligent data lake that securely retains historical exposure and remediation data to support audits, forensics, trend analysis, and AI-driven decisioning. By preserving years of context, not just point-in-time snapshots, Brinqa enables teams to understand how risk evolves, why decisions were made, and what impact remediation actions delivered, all while maintaining customer ownership and control of data.

An orchestration layer to act and remediate faster

The Orchestration Layer is where insights are visualized, decisions are made, and actions are executed.

Out-of-the-box dashboards provide immediate visibility into exposure priorities, including OWASP Top 10, top findings by team or business unit, urgency, class, and threat, helping teams focus on what matters most.

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, reduce manual effort, and drive measurable outcomes.