AGENTIC EXPOSURE MANAGEMENT
Autonomous AI Agents for Your Entire VM Lifecycle
Attackers are moving faster than humans can patch, but most of security work is still manual: investigating exposures, chasing asset owners, validating exploitability, writing reports. Zafran automates those steps end-to-end to continuously discover exposures, correlate context, and trigger remediation through your existing tools.
Detect zero days the moment they appear (before scanners even catch up).
Automatically validate exploitability and eliminate false positives.
Identify asset owners and generate audit-ready reports without manual effort.
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A New Model For Exposure Management Using Agentic AI
Autonomous AI agents for your entire vulnerability management lifecycle
Use Case: Top Exploitable Vulnerabilities
Ask once. See your riskiest vulnerabilities instantly. Instead of combing through dashboards or spreadsheets, simply ask: “Show me my most exploitable vulnerabilities.” Zafran responds with context-rich answers that drive immediate action. In a single query, Zafran’s AI can help you:
- Correlate vulnerabilities, internet reachability, control misconfigurations, and critical-asset context to surface toxic combinations of exposures most likely to be exploited right now.
- Visualize how attackers could move through your environment, from entry point to impact, with mapped MITRE techniques.
- Filter by business unit, asset type, or criticality to focus on what truly matters to your organization.
Use Case: Zero-Day Exposure Hunting
Stay ahead of attackers by identifying and mitigating newly disclosed vulnerabilities before they’re exploited in the wild. Zafran’s agentic AI turns zero-day response from reactive scrambling into proactive resilience by:
- Modeling exposure at the component level using SBOM inventory and dependency intelligence to locate affected libraries and packages across your environment.
- Correlating threat intelligence, runtime signals, and exposure paths to identify assets at risk from newly emerging vulnerabilities, even before a scan is conducted.
- Generating mitigation plans automatically to reduce exposure until a vendor patch or updated package becomes available.
Use Case: Exploitability Validation
Not every vulnerability deserves attention. Zafran proves which ones do. By fusing CVE intelligence with real-time access to the impacted asset, Zafran’s AI separates truly exploitable flaws from harmless noise, enabling teams to:
- Validate exploitability in real environments by confirming whether the specific configurations, permissions, and conditions exist that make exploitation possible.
- Automatically close invalid findings with embedded proof, saving analysts hours of manual review and false-positive triage.
- Focus remediation on vulnerabilities that are actually reachable and relevant to attacker techniques and operational realities.
Use Case: Asset Ownership
The fastest remediation starts with knowing who owns what. Zafran’s AI automatically maps ownership across infrastructure, code, and users, ending the manual detective work by:
- Correlating tags, login traces, communication patterns, and change trails to identify asset owners with high confidence.
- Reducing time-to-remediation by routing the right issue to the right team automatically.
- Eliminating guesswork and ticket ping-pong across IT, security, and engineering.
Use Case: Impact Analysis
Before deploying a patch, Zafran’s agentic AI maps dependencies, assesses risk deltas, and forecasts potential impact, giving teams the confidence to act without guesswork. Leveraging Zafran’s impact engine allows organizations to:
- Generate ready-to-execute remediation plans with before/after risk clearly visualized.
- Identify dependencies and assess whether patches could disrupt critical systems or services.
- Deliver precise, low-noise recommendations that accelerate safe, effective remediation.
Use Case: Reporting
Automate the compliance and communication layer your team dreads. Zafran’s automated reporting layer delivers the ability to:
- Generate evidence-backed reports with timestamps, validation data, and resolution context.
- Maintain real-time visibility into SLAs, risk posture, and remediation progress.
- Give executives and auditors a single source of truth for exposure management.
AI is Only As Good As Your Data
AI-Native Exposure Graph Continuously Maps Exposures to Compensating Controls
Trust and Guardrails
All customer data stays within Zafran’s secure AWS environment, where the AI Agent operates entirely inside Zafran’s tenancy and data is never used to train AI models. External access is tightly controlled; the agent cannot browse the internet or send raw data externally, and any optional web search is human-approved and tenant-specific, with strict tenant isolation ensuring complete separation between customers.
Zafran enforces multiple layers of safety and control, including AWS Bedrock Guardrails for content filtering, Human-in-the-Loop approvals for sensitive actions like remote commands or web searches, and strict input validation before any tool execution. All activity is fully audit-logged for compliance and monitoring, with robust error handling and retry logic ensuring reliability and resilience.
See Zafran in Action
Prioritize and fix what is truly exploitable using risk context from your existing security tools
