AI across the entire DAST lifecycle.
VulnSign does not bolt a chatbot onto scan results. AI helps manage attack surface, choose testing strategy, triage live findings, connect exploit paths, and communicate risk—while keeping the analyst in control.
Technology detection that starts before the first request—and gets stronger after it.
Auto-detect technology stack (AI)
When enabled while creating a target, VulnSign launches a complete evidence-driven research, correlation, and policy-generation workflow after creation.
This is not a one-shot framework guess. Public intelligence, organizational hiring signals, runtime observations, security history, and DAST-native fingerprints become one traceable technology model.
- 01
Research the domain
Begin an AI-assisted OSINT investigation immediately after the target is created.
- 02
Search public evidence
Analyze Google filetype results and technology signals found in LinkedIn job postings.
- 03
Identify the application stack
Determine languages, frameworks, databases, and web-server technologies.
- 04
Map delivery infrastructure
Detect CDN, WAF, hosting, edge, and supporting infrastructure technologies.
- 05
Research security history
Find known security incidents, disclosed weaknesses, and relevant technology risks.
- 06
Correlate runtime evidence
Merge OSINT conclusions with crawler observations and passive-scan findings.
- 07
Normalize DAST technology keys
Map discovered products and versions to the technology identifiers used by the DAST engine.
- 08
Build a target-specific policy
Create an optimized scan policy based on the verified attack surface and technology stack.
- 09
Preserve evidence and confidence
Explain the source and reasoning behind every technology detection.
- 010
Summarize in the selected language
Present the research and recommended strategy in the language chosen by the user.
- 011
Run with Claude or Ollama
Use advanced cloud reasoning or keep the complete enrichment workflow local.
OSINT
Search · LinkedIn · incidents
Runtime
Crawler · headers · cookies · TLS
Correlation
Evidence · confidence · versions
DAST mapping
Technology keys · test coverage
Output
Optimized policy · localized summary
Provider
Claude / Ollama
Result
Evidence-backed policy
Cloud intelligence when you want it. Local inference when you need it.
Connect Claude for advanced reasoning or run compatible models with Ollama on your own machine. Local prompts, responses, and model weights stay inside your infrastructure, with no per-call API cost.
Claude
Advanced hosted reasoning
Ollama Local
Private, offline inference
Configure your AI runtime
Show Claude and local Ollama providers, usage visibility, and data security controls.
01
Observe
02
Reason
03
Act
Move from intent to an executable security workflow.
Describe the outcome. VulnSign turns it into targets, groups, technology context, scan decisions, and follow-up actions.
Natural-language targets
Add, group, enrich, and scan targets from a single plain-language instruction.
Adaptive scan strategy
Use detected technologies and application context to recommend profiles and policies.
End-to-end orchestration
Chain target discovery, technology detection, policy selection, scanning, and reporting.
Subdomain intelligence
Analyze takeover risk, shadow IT, DNS posture, and the potential attack surface.
Example instruction
“Scan example.com, detect its technology first, select the right profile, and focus on authentication bypass and IDOR.”
Route each operation to the right model
Show per-task models and reasoning levels for triage, summaries, attack chains, OSINT, and Android.
01
Observe
02
Reason
03
Act
Match model depth to the security decision.
Use a fast model for high-volume triage and deeper reasoning for attack chains. Configure independent models and thinking levels for summaries, issue chat, OSINT, stack analysis, Android crawling, and network risk.
- Inline triage
- Executive summaries
- Issue reasoning
- Attack chains
- OSINT & stack tech
- Android crawler
Evidence-backed stack analysis
Correlate OSINT research with crawler and passive-scan evidence, then explain every conclusion.
Real-time alert triage
Classify likely false positives in the background while preserving analyst oversight.
Attack chain analysis
Reason across findings to expose multi-step paths that isolated alerts cannot show.
Adaptive payloads
Generate technology- and WAF-aware payload candidates for deeper manual validation.
Explain risk at both analyst and executive depth.
Generate scan intelligence on demand, then choose exactly which AI-authored content belongs in the final report.
Generate scan intelligence
Show Executive Summary and Attack Chain Analysis operating on completed findings.
01
Observe
02
Reason
03
Act
Build an AI-enriched report
Show optional AI content being selected while generating a customer-ready security report.
01
Observe
02
Reason
03
Act
Executive Summary
Turn findings into a concise risk narrative for technical or executive audiences.
Attack Chain Analysis
Correlate vulnerabilities into plausible multi-step exploitation paths.
Report enrichment
Optionally include AI narratives alongside evidence, remediation, and attack surface.
Put AI inside the work—not outside the evidence.
Start with local inference or connect advanced cloud reasoning, then choose where AI participates in each workspace.