Cap 1
Top Exploitable Vulnerabilities
An agent continuously assembles the short list of exposures attackers can use right now, with evidence and a recommended action.
UI · agent run
CTEM Lifecycle · 06
Agentic Exposure Management
Zafran agents run the exposure lifecycle end to end: they hunt zero-day exposure, validate exploitability, find asset owners, analyze impact and draft the report, then wait for a human to approve the action. Not a chatbot; autonomous workflows with guardrails.
Capabilities
How Zafran Enables Agentic Exposure Management
Autonomous AI agents that investigate, validate and mobilize across the entire lifecycle with human oversight.
Cap 2
Zero-Day Exposure Hunting
When a new CVE or exploit drops, the agent checks runtime presence, reachability and control coverage across the estate in minutes.
UI · hunt log
Cap 3
Exploitability Validation
The agent proves or rules out exploitability step by step, then routes the verdict into mitigation or remediation.
UI · validation steps
Cap 4
Asset Ownership
The agent resolves who owns an exposed asset from CMDB, cloud tags, code repositories and ticket history.
UI · ownership graph
Use cases
All six use cases
Each agent runs the same loop: trigger, investigate, gather evidence, propose, wait for approval. One UI micro-animation per tile.
Use case 1
Top Exploitable Vulnerabilities
A living short list of what attackers can exploit today, refreshed as runtime, reachability and intel change.
UI · agent run
Use case 2
Zero-Day Exposure Hunting
Minutes after disclosure: which assets load the component, which are reachable, which controls already block it.
UI · hunt log
Use case 3
Exploitability Validation
Evidence-backed verdicts per exposure, handed to mitigation or remediation with the reasoning attached.
UI · validation steps
Use case 4
Asset Ownership
Owner resolution across CMDB, tags, repos and tickets so every fix has a name on it.
UI · ownership graph
Use case 5
Impact Analysis
Trace what an exploited asset could reach: connected systems, data stores and business services.
UI · blast radius
Use case 6
Reporting
Agents draft the exposure report with evidence, timestamps and trend deltas, ready for a human to review and send.
Workflow
How it works
- 01
Trigger
A new CVE, a threat-intel alert, a schedule or an analyst request starts an agent run.
- 02
Investigate and validate
The agent queries the Exposure Graph, runtime data, reachability and control state, and records its evidence.
- 03
Propose, approve, mobilize
It proposes mitigation or remediation, an approver signs off, and the action flows through your existing tools.
Compare
Zafran vs. the alternatives
Zafran runs the whole lifecycle on one Exposure Graph; alternatives cover a slice of it.
| Capability | Zafran agents | Chatbot copilots | SOAR / automation playbooks |
|---|---|---|---|
| Autonomous multi-step investigation, not Q&A | |||
| Grounded in runtime, reachability and control data | |||
| Human-in-the-loop approval before action | |||
| Covers the full lifecycle, discovery through reporting | |||
| Tenant isolation, no customer-data model training | |||
| Zero-day hunting within minutes of disclosure |
Yes Partial NoIllustrative; final rows per stage TBD with PMM
Trust
Trust and guardrails
Autonomy with limits: agents recommend, humans approve, data stays put.
Data stays in your Zafran tenant
Customer data is processed in Zafran's AWS environment with strict per-tenant isolation.
No model training on customer data
Zafran does not use customer data to train or fine-tune models.
Human-in-the-loop by default
Agents investigate and prepare actions; a named approver signs off before anything changes in your environment.
Proof
What customers see
6
agentic use cases, from zero-day hunting to reporting
Minutes
to hunt a new zero-day across the estate
100%
of actions gated by human approval
“Zafran enhanced our controls, enabling us to position ourselves with exploit and zero-day countermeasures.”
James Robinson
CISO, Netskope
Case study · Manufacturing
Fortune 1000 Manufacturer cut MTTR with agentic AI
Read how the team ran this stage of the lifecycle with Zafran.
FAQ
Agentic Exposure Management: questions buyers ask
What is agentic exposure management?
The use of autonomous AI agents to run the threat exposure management lifecycle: discovering and validating exposures, finding owners, analyzing impact, proposing mitigation or remediation and drafting reports, with a human approving actions. Zafran agents operate on the same Exposure Graph as the rest of the platform.
How is this different from an AI security copilot or chatbot?
A copilot answers questions when asked. Zafran agents run multi-step workflows on their own triggers, gather evidence from runtime, reachability and control data, and hand a human a decision, not a chat transcript.
Can Zafran agents take action in my environment without approval?
By default, no. Agents investigate, validate and prepare actions; a named approver signs off before a control is changed or a ticket is executed. Approval policies are configurable per customer.
Where is my data processed, and is it used to train AI models?
Data is processed in Zafran's AWS environment with strict per-tenant isolation. Zafran does not train or fine-tune models on customer data.
Which use cases do Zafran agents cover today?
Top Exploitable Vulnerabilities, Zero-Day Exposure Hunting, Exploitability Validation, Asset Ownership, Impact Analysis and Reporting.
How do agents help with zero-day response?
On disclosure, the zero-day hunting agent checks which assets load the affected component, which are reachable, and which existing controls already block the exploit, then proposes mitigations for the rest, typically within minutes.
See Zafran in Action
Prioritize and fix what is truly exploitable using risk context from your existing security tools.