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- Created `social_media_analyzer/operational_security.py` with AI modules for Cloud, IoT, and OpSec. - Added `/analyze/cloud`, `/analyze/iot`, and `/analyze/opsec` endpoints to Flask backend. - Integrated the new "Operational Security" role into the `OfficialAssistance.jsx` frontend. - Added interactive tool launching and results display for the new security tools. - Updated `Marketplace.jsx` to reflect the expanded support capabilities. - Added unit tests for the new backend modules. Co-authored-by: GYFX35 <134739293+GYFX35@users.noreply.github.com>
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Reviewer's GuideAdds a new 'Operational Security' role with Cloud/IoT/OpSec AI analysis across frontend and backend, exposing Flask endpoints for security scans, wiring them into the Official Assistance UI, and covering behavior with unit tests. Sequence diagram for launching an Operational Security Cloud Guard scansequenceDiagram
actor User
participant Browser_OfficialAssistance as Browser_OfficialAssistanceUI
participant Flask_App as Flask_App
participant Operational_Security as operational_security_module
participant CloudSecurityAI as CloudSecurityAI
participant Sensitive_Scanner as sensitive_data_scanner
User->>Browser_OfficialAssistance: Click Launch on Cloud_Guard
Browser_OfficialAssistance->>Browser_OfficialAssistance: handleLaunch(toolId, toolName)
Browser_OfficialAssistance->>Flask_App: POST /analyze/cloud
activate Flask_App
Flask_App->>Flask_App: analyze_cloud()
Flask_App->>Flask_App: parse JSON and validate content
Flask_App->>Operational_Security: analyze_cloud_security(content)
activate Operational_Security
Operational_Security->>CloudSecurityAI: create scanner instance
Operational_Security->>CloudSecurityAI: scan_content(text_content)
activate CloudSecurityAI
CloudSecurityAI->>Sensitive_Scanner: iterate SENSITIVE_DATA_PATTERNS
Sensitive_Scanner-->>CloudSecurityAI: pattern matches
CloudSecurityAI-->>Operational_Security: findings
deactivate CloudSecurityAI
Operational_Security-->>Flask_App: findings JSON
deactivate Operational_Security
Flask_App-->>Browser_OfficialAssistance: JSON response
deactivate Flask_App
Browser_OfficialAssistance->>Browser_OfficialAssistance: setAnalysisResult(response)
Browser_OfficialAssistance-->>User: Render AI Analysis Output panel
Sequence diagram for IoT and OpSec analysis endpointssequenceDiagram
participant Browser_OfficialAssistance as Browser_OfficialAssistanceUI
participant Flask_App as Flask_App
participant Operational_Security as operational_security_module
participant IoTSecurityAI as IoTSecurityAI
participant OpSecAI as OpSecAI
participant InfrastructureProtectionAI as InfrastructureProtectionAI
rect rgb(230,230,250)
Browser_OfficialAssistance->>Flask_App: POST /analyze/iot with device_data
activate Flask_App
Flask_App->>Operational_Security: analyze_iot_security(device_data)
activate Operational_Security
Operational_Security->>IoTSecurityAI: create scanner
Operational_Security->>IoTSecurityAI: analyze_telemetry(device_data)
activate IoTSecurityAI
IoTSecurityAI->>InfrastructureProtectionAI: detect_iot_tampering(device_data)
InfrastructureProtectionAI-->>IoTSecurityAI: tampering_assessment
IoTSecurityAI-->>Operational_Security: analysis_result
deactivate IoTSecurityAI
Operational_Security-->>Flask_App: analysis_result
deactivate Operational_Security
Flask_App-->>Browser_OfficialAssistance: JSON result
deactivate Flask_App
end
rect rgb(220,255,220)
Browser_OfficialAssistance->>Flask_App: POST /analyze/opsec with logs
activate Flask_App
Flask_App->>Operational_Security: analyze_opsec_security(logs)
activate Operational_Security
Operational_Security->>OpSecAI: create scanner
Operational_Security->>OpSecAI: analyze_logs(log_entries)
activate OpSecAI
OpSecAI-->>Operational_Security: status score findings
deactivate OpSecAI
Operational_Security-->>Flask_App: summarized_risk
deactivate Operational_Security
Flask_App-->>Browser_OfficialAssistance: JSON result
deactivate Flask_App
end
Class diagram for new Operational Security AI componentsclassDiagram
class CloudSecurityAI {
+scan_content(text_content)
}
class IoTSecurityAI {
-infra_protection
+IoTSecurityAI()
+analyze_telemetry(device_data)
}
class OpSecAI {
-SUSPICIOUS_OPSEC_PATTERNS
+analyze_logs(log_entries)
}
class InfrastructureProtectionAI {
+detect_iot_tampering(device_data)
}
class operational_security_module {
+analyze_cloud_security(content)
+analyze_iot_security(device_data)
+analyze_opsec_security(logs)
}
class sensitive_data_scanner_module {
+SENSITIVE_DATA_PATTERNS
}
operational_security_module ..> CloudSecurityAI : uses
operational_security_module ..> IoTSecurityAI : uses
operational_security_module ..> OpSecAI : uses
IoTSecurityAI o-- InfrastructureProtectionAI : composes
CloudSecurityAI ..> sensitive_data_scanner_module : reads_patterns
OpSecAI ..> re_module : uses_regex
class re_module {
}
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Hey - I've found 2 issues, and left some high level feedback:
- In
OfficialAssistance.jsx, the mapping fromtool.idto endpoint and request body is hard-coded insidehandleLaunch; consider moving this configuration into thetoolsdefinition (e.g., endpoint/payload builder per tool) so it’s easier to extend and keeps UI logic more declarative. - The Flask
/analyze/cloud,/analyze/iot, and/analyze/opsecroutes directly calloperational_securitywithout guarding against exceptions; adding a narrow try/except around the analyzer calls with a structured error response would make the API more resilient to backend failures. - The simulated cloud content in
handleLaunchincludes strings that resemble real AWS/Google keys; even if they are fake, consider making them clearly non-production patterns (e.g., shorter/obviously invalid formats) to avoid accidental triggering of scanners or future policy checks against committed secrets.
Prompt for AI Agents
Please address the comments from this code review:
## Overall Comments
- In `OfficialAssistance.jsx`, the mapping from `tool.id` to endpoint and request body is hard-coded inside `handleLaunch`; consider moving this configuration into the `tools` definition (e.g., endpoint/payload builder per tool) so it’s easier to extend and keeps UI logic more declarative.
- The Flask `/analyze/cloud`, `/analyze/iot`, and `/analyze/opsec` routes directly call `operational_security` without guarding against exceptions; adding a narrow try/except around the analyzer calls with a structured error response would make the API more resilient to backend failures.
- The simulated cloud content in `handleLaunch` includes strings that resemble real AWS/Google keys; even if they are fake, consider making them clearly non-production patterns (e.g., shorter/obviously invalid formats) to avoid accidental triggering of scanners or future policy checks against committed secrets.
## Individual Comments
### Comment 1
<location path="text_message_analyzer/app.py" line_range="80-86" />
<code_context>
+ result = operational_security.analyze_iot_security(device_data)
+ return jsonify(result)
+
+@app.route('/analyze/opsec', methods=['POST'])
+def analyze_opsec():
+ data = request.get_json()
+ if not data or 'logs' not in data:
+ return jsonify({"error": "Missing 'logs' in request body"}), 400
+
+ logs = data['logs']
+ result = operational_security.analyze_opsec_security(logs)
+ return jsonify(result)
</code_context>
<issue_to_address>
**issue (bug_risk):** Validate the type/shape of `logs` to avoid treating a string as an iterable of characters.
Right now we only check that `logs` exists, then pass it directly to `operational_security.analyze_opsec_security`, which does `"\n".join(log_entries)`. If a client sends a single string instead of a list of log lines, the join will run over characters, corrupting the input for pattern matching. Please validate that `logs` is a list (or iterable) of strings and return a 400 when the type is invalid.
</issue_to_address>
### Comment 2
<location path="social_media_analyzer/operational_security.py" line_range="8-14" />
<code_context>
+class CloudSecurityAI:
+ """AI for scanning cloud credentials and sensitive information."""
+
+ def scan_content(self, text_content):
+ findings = {}
+ for pattern_name, regex in SENSITIVE_DATA_PATTERNS.items():
+ matches = regex.findall(text_content)
+ if matches:
+ findings[pattern_name] = matches
+ return findings
+
+class IoTSecurityAI:
</code_context>
<issue_to_address>
**🚨 issue (security):** Avoid returning full sensitive matches to the client to reduce credential exposure risk.
`scan_content` returns the full regex matches from `SENSITIVE_DATA_PATTERNS`, so any detected keys or credentials are sent back to the caller. This unnecessarily exposes secrets via the API (and potentially logs or client storage). Consider returning only redacted values, counts, or metadata (e.g., type and position), or making raw-return behavior explicitly configurable with a secure default.
</issue_to_address>Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews.
- Implemented credential redaction in `CloudSecurityAI` to avoid exposing full secrets. - Added robust error handling and type validation to Flask security endpoints. - Refactored `OfficialAssistance.jsx` to move tool configurations into a declarative structure. - Replaced realistic-looking simulated credentials with obviously fake ones. - Updated unit tests to verify redaction logic. Co-authored-by: GYFX35 <134739293+GYFX35@users.noreply.github.com>
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I have enhanced the Global Security Platform by adding a comprehensive 'Operational Security' role. This includes backend AI logic for scanning cloud credentials (AWS, GCP, etc.), monitoring IoT device telemetry for tampering, and analyzing operational logs for security threats. I exposed these features through new Flask API endpoints and created a new tab in the 'Official Assistance' UI where users can launch these AI tools and see real-time analysis results. Verifications included unit tests and visual confirmation with Playwright.
PR created automatically by Jules for task 4755552451855147053 started by @GYFX35
Summary by Sourcery
Add an Operational Security role with AI-backed cloud, IoT, and log analysis surfaced through new Official Assistance UI tools and Flask API endpoints.
New Features:
Enhancements:
Tests: