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Intelligence View

⚡ tsecurity.de Intelligence

I built a browser-only AI workflow safety prototype — looking for feedback

I’m building a small public-safe prototype called WorkflowGuard AI. The idea is simple: Before a team connects AI to real customer data, internal tools, or production workflows, they should be able to preview whether an AI workflow idea i…

Sichere Programmierung dev.to 08.07.2026, 14:01 Uhr 2 Min. Lesezeit Cloud & IT-Enterprise

↗ Quelle (dev.to)

Reagiere als Erste:r — dein Feedback zählt!

I’m building a small public-safe prototype called WorkflowGuard AI.

The idea is simple:

Before a team connects AI to real customer data, internal tools, or production workflows, they should be able to preview whether an AI workflow idea is safe enough to test.

The demo turns one AI workflow idea into a first-step review:

risk level

safety snapshot

safe first test

human review gate

validation checklist

trace note

Live demo:

https://chuangmenghuan-wq.github.io/traceable-ai-workflow-prototype/

GitHub:

https://github.com/chuangmenghuan-wq/traceable-ai-workflow-prototype

Important boundary:

This is not a finished SaaS product.

It is not production automation.

It does not connect to real customer data.

It has no login, no backend, no storage, and no AI API calls.

The current version is intentionally browser-only and static. I’m testing whether the workflow framing is understandable before adding anything more complex.

I’m looking for feedback from developers, builders, and people who have worked with AI tools inside real workflows:

Can you understand what this is within 3 minutes?

Does the demo feel useful, or too abstract?

Would this help a small team decide where to start with AI?

Is the public-safe boundary clear?

What would make the demo more useful?

I’m especially interested in feedback on the framing, not just the UI.

Thanks for taking a look.

IR-PLAYBOOK-VULN-REMEDIATIONMEDIUM

Schritte anzeigen

SOC Incident Playbook: Vulnerability Remediation & Verification

Asset Inventory

Asset-Datenbank nach betroffenen Komponenten, Versionen und exposed Ports abfragen.

nmap -sV --script vuln -p- <target-network>

Exploit MonitoringSofort

SIEM-Regeln und IDS-Signaturen (Snort/Suricata) auf Angriffsversuche schärfen.

tail -f /var/log/suricata/fast.log | grep <cve>

Patch DeploymentSofort

Patch oder offiziellen Workaround gemäß Hersteller-Empfehlung einspielen.

Check Vendor Security Advisory Bulletin

1-Click Detection Engineering: Sigma & YARA RulesSOC Ready

Regeln anzeigen

title: Detect Exploitation - I built a browser-only AI workflow safety prototype — looking for feedback
id: 8afb43e3-7195-4c37-a354-d3ff041f4d01
status: experimental
description: Automatisch generierte SIEM-Erkennungsregel basierend auf CTI Intelligence
references:
  - https://tsecurity.de/
author: iShareStuff CTI Automated Detection Engine
date: 2026-09-24
logsource:
  category: network_connection
  product: any
detection:
  selection:
      CommandLine|contains:
        - 'exploit'
  condition: selection
falsepositives:
  - Legitime administrative Zugriffe oder Penetrationstests
level: high
tags:
  - attack.initial_access

rule CTI_Threat_Indicator {
    meta:
        author = "iShareStuff CTI Automated Detection Engine"
        date = "2026-09-24"
        description = "YARA Signature for "
    strings:
        $str = "I built a browser-only AI work" ascii wide
    condition:
        any of them
}

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Fokus-Vektor:

Kognitive Analyse für identifizierte Bedrohung: Erhöhte Bedrohungslage im Bereich I built a browser-only AI workflow safet.... Basierend auf 368k Vektor-Korrelationen werden sofortige Isolationsmaßnahmen für betroffene Endpunkte empfohlen.

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Format: python

#!/usr/bin/env python3
# Exploit Title: PoC for Target
import socket, sys
target = '192[.]168[.]1[.]10'
url = 'hxxp://target[.]local/vuln'
s = socket.socket()
s.connect((target, 80))
s.send(b'GET / HTTP/1.1\r\nHost: target.local\r\n\r\n')

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