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intercept/utils/tscm/reports.py
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James Smith 96172ca593 style: apply ruff-format to entire codebase 
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Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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"""
TSCM Report Generation Module
Generates:
1. Client-safe PDF reports with executive summary
2. Technical annex (JSON + CSV) with device timelines and indicators
DISCLAIMER: All reports include mandatory disclaimers.
No packet data. No claims of confirmed surveillance.
"""
from __future__ import annotations
import csv
import io
import logging
from dataclasses import dataclass, field
from datetime import datetime
from utils.tscm.signal_classification import (
SIGNAL_ANALYSIS_DISCLAIMER,
assess_signal,
generate_hedged_statement,
)
logger = logging.getLogger("intercept.tscm.reports")
# =============================================================================
# Report Data Structures
# =============================================================================
@dataclass
class ReportFinding:
"""A single finding for the report."""
identifier: str
protocol: str
name: str | None
risk_level: str
risk_score: int
description: str
indicators: list[dict] = field(default_factory=list)
recommended_action: str = ""
playbook_reference: str = ""
# Signal classification data
signal_strength: str | None = None # minimal, weak, moderate, strong, very_strong
signal_confidence: str | None = None # low, medium, high
signal_interpretation: str | None = None
signal_caveats: list[str] = field(default_factory=list)
@dataclass
class ReportMeetingSummary:
"""Meeting window summary for report."""
name: str | None
start_time: str
end_time: str | None
duration_minutes: float
devices_first_seen: int
behavior_changes: int
high_interest_devices: int
@dataclass
class TSCMReport:
"""
Complete TSCM sweep report.
Contains all data needed for both client-safe PDF and technical annex.
"""
# Report metadata
report_id: str
generated_at: datetime
sweep_id: int
sweep_type: str
# Location and context
location: str | None = None
examiner_name: str = ""
baseline_id: int | None = None
baseline_name: str | None = None
# Executive summary
executive_summary: str = ""
overall_risk_assessment: str = "low" # low, moderate, elevated, high
key_findings_count: int = 0
# Capabilities used
capabilities: dict = field(default_factory=dict)
limitations: list[str] = field(default_factory=list)
# Findings by risk tier
high_interest_findings: list[ReportFinding] = field(default_factory=list)
needs_review_findings: list[ReportFinding] = field(default_factory=list)
informational_findings: list[ReportFinding] = field(default_factory=list)
# Meeting window summaries
meeting_summaries: list[ReportMeetingSummary] = field(default_factory=list)
# Statistics
total_devices_scanned: int = 0
wifi_devices: int = 0
wifi_clients: int = 0
bluetooth_devices: int = 0
rf_signals: int = 0
new_devices: int = 0
missing_devices: int = 0
# Sweep duration
sweep_start: datetime | None = None
sweep_end: datetime | None = None
duration_minutes: float = 0.0
# Technical data (for annex only)
device_timelines: list[dict] = field(default_factory=list)
all_indicators: list[dict] = field(default_factory=list)
baseline_diff: dict | None = None
correlation_data: list[dict] = field(default_factory=list)
# =============================================================================
# Disclaimer Text
# =============================================================================
REPORT_DISCLAIMER = """
IMPORTANT DISCLAIMER
This report documents the findings of a Technical Surveillance Countermeasures
(TSCM) sweep conducted using electronic detection equipment. The following
limitations and considerations apply:
1. DETECTION LIMITATIONS: No TSCM sweep can guarantee detection of all
surveillance devices. Sophisticated devices may evade detection.
2. FINDINGS ARE INDICATORS: All findings represent patterns and indicators,
NOT confirmed surveillance devices. Each finding requires professional
interpretation and may have legitimate explanations.
3. ENVIRONMENTAL FACTORS: Wireless signals are affected by building
construction, interference, and other environmental factors that may
impact detection accuracy.
4. POINT-IN-TIME ASSESSMENT: This report reflects conditions at the time
of the sweep. Conditions may change after the assessment.
5. NOT LEGAL ADVICE: This report does not constitute legal advice. Consult
qualified legal counsel for guidance on surveillance-related matters.
6. PRIVACY CONSIDERATIONS: Some detected devices may be legitimate personal
devices of authorized individuals.
This report should be treated as confidential and distributed only to
authorized personnel on a need-to-know basis.
"""
ANNEX_DISCLAIMER = """
TECHNICAL ANNEX DISCLAIMER
This annex contains detailed technical data from the TSCM sweep. This data
is provided for documentation and audit purposes.
- No raw packet captures or intercepted communications are included
- Device identifiers (MAC addresses) are included for tracking purposes
- Signal strength values are approximate and environment-dependent
- Timeline data is time-bucketed to preserve privacy
- All interpretations require professional TSCM expertise
This data should be handled according to organizational data protection
policies and applicable privacy regulations.
"""
# =============================================================================
# Report Generation Functions
# =============================================================================
def generate_executive_summary(report: TSCMReport) -> str:
"""Generate executive summary text."""
lines = []
# Opening
lines.append(f"TSCM Sweep Report - {report.location or 'Location Not Specified'}")
lines.append(f"Conducted: {report.sweep_start.strftime('%Y-%m-%d %H:%M') if report.sweep_start else 'Unknown'}")
lines.append(f"Duration: {report.duration_minutes:.0f} minutes")
lines.append("")
# Overall assessment
assessment_text = {
"low": "No significant indicators of surveillance activity were detected.",
"moderate": "Some devices require review but no confirmed surveillance indicators.",
"elevated": "Multiple indicators warrant further investigation.",
"high": "Significant indicators detected requiring immediate attention.",
}
lines.append(f"OVERALL ASSESSMENT: {report.overall_risk_assessment.upper()}")
lines.append(assessment_text.get(report.overall_risk_assessment, ""))
lines.append("")
# Key statistics
lines.append("SCAN STATISTICS:")
lines.append(f" - Total devices scanned: {report.total_devices_scanned}")
lines.append(f" - WiFi access points: {report.wifi_devices}")
lines.append(f" - WiFi clients: {report.wifi_clients}")
lines.append(f" - Bluetooth devices: {report.bluetooth_devices}")
lines.append(f" - RF signals: {report.rf_signals}")
lines.append("")
# Findings summary
lines.append("FINDINGS SUMMARY:")
lines.append(f" - High Interest (require investigation): {len(report.high_interest_findings)}")
lines.append(f" - Needs Review: {len(report.needs_review_findings)}")
lines.append(f" - Informational: {len(report.informational_findings)}")
lines.append("")
# Baseline comparison if available
if report.baseline_name:
lines.append(f"BASELINE COMPARISON (vs '{report.baseline_name}'):")
lines.append(f" - New devices: {report.new_devices}")
lines.append(f" - Missing devices: {report.missing_devices}")
lines.append("")
# Meeting window summary if available
if report.meeting_summaries:
lines.append("MEETING WINDOW ACTIVITY:")
for meeting in report.meeting_summaries:
lines.append(
f" - {meeting.name or 'Unnamed meeting'}: "
f"{meeting.devices_first_seen} new devices, "
f"{meeting.high_interest_devices} high interest"
)
lines.append("")
# Limitations
if report.limitations:
lines.append("SWEEP LIMITATIONS:")
for limit in report.limitations[:3]: # Top 3 limitations
lines.append(f" - {limit}")
lines.append("")
return "\n".join(lines)
def generate_findings_section(findings: list[ReportFinding], title: str) -> str:
"""Generate a findings section for the report with confidence-safe language."""
if not findings:
return f"{title}\n\nNo findings in this category.\n"
lines = [title, "=" * len(title), ""]
for i, finding in enumerate(findings, 1):
lines.append(f"{i}. {finding.name or finding.identifier}")
lines.append(f" Protocol: {finding.protocol.upper()}")
lines.append(f" Identifier: {finding.identifier}")
lines.append(f" Risk Score: {finding.risk_score}")
# Signal classification with confidence
if finding.signal_strength:
confidence_label = (finding.signal_confidence or "low").capitalize()
strength_label = finding.signal_strength.replace("_", " ").title()
lines.append(f" Signal: {strength_label} (Confidence: {confidence_label})")
lines.append(f" Assessment: {finding.description}")
# Interpretation with hedged language
if finding.signal_interpretation:
lines.append(f" Interpretation: {finding.signal_interpretation}")
if finding.indicators:
lines.append(" Indicators:")
for ind in finding.indicators[:5]: # Limit to 5 indicators
lines.append(f" - {ind.get('type', 'unknown')}: {ind.get('description', '')}")
lines.append(f" Recommended Action: {finding.recommended_action}")
if finding.playbook_reference:
lines.append(f" Reference: {finding.playbook_reference}")
# Include relevant caveats for high-interest findings
if finding.signal_caveats and finding.risk_level == "high_interest":
lines.append(" Note: " + finding.signal_caveats[0])
lines.append("")
return "\n".join(lines)
def generate_meeting_section(summaries: list[ReportMeetingSummary]) -> str:
"""Generate meeting window summary section."""
if not summaries:
return "MEETING WINDOW SUMMARY\n\nNo meeting windows were marked during this sweep.\n"
lines = ["MEETING WINDOW SUMMARY", "=" * 22, ""]
for meeting in summaries:
lines.append(f"Meeting: {meeting.name or 'Unnamed'}")
lines.append(f" Time: {meeting.start_time} - {meeting.end_time or 'ongoing'}")
lines.append(f" Duration: {meeting.duration_minutes:.0f} minutes")
lines.append(f" Devices first seen during meeting: {meeting.devices_first_seen}")
lines.append(f" Behavior changes detected: {meeting.behavior_changes}")
lines.append(f" High interest devices active: {meeting.high_interest_devices}")
if meeting.devices_first_seen > 0 or meeting.high_interest_devices > 0:
lines.append(" NOTE: Meeting-correlated activity detected - see findings for details")
lines.append("")
lines.append("Meeting-correlated activity indicates temporal correlation only.")
lines.append("Devices appearing during meetings may have legitimate explanations.")
lines.append("")
return "\n".join(lines)
def generate_pdf_content(report: TSCMReport) -> str:
"""
Generate complete PDF report content.
Returns plain text that can be converted to PDF.
For actual PDF generation, use a library like reportlab or weasyprint.
"""
sections = []
# Header
sections.append("=" * 70)
sections.append("TECHNICAL SURVEILLANCE COUNTERMEASURES (TSCM) SWEEP REPORT")
sections.append("=" * 70)
sections.append("")
sections.append(f"Report ID: {report.report_id}")
sections.append(f"Generated: {report.generated_at.strftime('%Y-%m-%d %H:%M:%S')}")
sections.append(f"Sweep ID: {report.sweep_id}")
if report.location:
sections.append(f"Site / Location: {report.location}")
if report.examiner_name:
sections.append(f"Examiner: {report.examiner_name}")
sections.append("")
# Executive Summary
sections.append("-" * 70)
sections.append("EXECUTIVE SUMMARY")
sections.append("-" * 70)
sections.append(report.executive_summary or generate_executive_summary(report))
sections.append("")
# High Interest Findings
if report.high_interest_findings:
sections.append("-" * 70)
sections.append(generate_findings_section(report.high_interest_findings, "HIGH INTEREST FINDINGS"))
# Needs Review Findings
if report.needs_review_findings:
sections.append("-" * 70)
sections.append(generate_findings_section(report.needs_review_findings, "FINDINGS REQUIRING REVIEW"))
# Meeting Window Summary
if report.meeting_summaries:
sections.append("-" * 70)
sections.append(generate_meeting_section(report.meeting_summaries))
# Capabilities & Limitations
sections.append("-" * 70)
sections.append("SWEEP CAPABILITIES & LIMITATIONS")
sections.append("=" * 33)
sections.append("")
if report.capabilities:
caps = report.capabilities
sections.append("Equipment Used:")
if caps.get("wifi", {}).get("mode") != "unavailable":
sections.append(f" - WiFi: {caps.get('wifi', {}).get('mode', 'unknown')} mode")
if caps.get("bluetooth", {}).get("mode") != "unavailable":
sections.append(f" - Bluetooth: {caps.get('bluetooth', {}).get('mode', 'unknown')}")
if caps.get("rf", {}).get("available"):
sections.append(f" - RF/SDR: {caps.get('rf', {}).get('device_type', 'unknown')}")
sections.append("")
if report.limitations:
sections.append("Limitations:")
for limit in report.limitations:
sections.append(f" - {limit}")
sections.append("")
# Signal Analysis Note
sections.append("-" * 70)
sections.append("SIGNAL ANALYSIS METHODOLOGY")
sections.append("=" * 27)
sections.append(SIGNAL_ANALYSIS_DISCLAIMER.strip())
sections.append("")
# Disclaimer
sections.append("-" * 70)
sections.append(REPORT_DISCLAIMER)
# Footer
sections.append("")
sections.append("=" * 70)
sections.append("END OF REPORT")
sections.append("=" * 70)
return "\n".join(sections)
def generate_technical_annex_json(report: TSCMReport) -> dict:
"""
Generate technical annex as JSON.
Contains detailed device timelines, all indicators, and raw data
for audit and further analysis.
"""
return {
"annex_type": "tscm_technical_annex",
"report_id": report.report_id,
"generated_at": report.generated_at.isoformat(),
"sweep_id": report.sweep_id,
"disclaimer": ANNEX_DISCLAIMER.strip(),
"sweep_details": {
"type": report.sweep_type,
"location": report.location,
"start_time": report.sweep_start.isoformat() if report.sweep_start else None,
"end_time": report.sweep_end.isoformat() if report.sweep_end else None,
"duration_minutes": report.duration_minutes,
"baseline_id": report.baseline_id,
"baseline_name": report.baseline_name,
},
"capabilities": report.capabilities,
"limitations": report.limitations,
"statistics": {
"total_devices": report.total_devices_scanned,
"wifi_devices": report.wifi_devices,
"wifi_clients": report.wifi_clients,
"bluetooth_devices": report.bluetooth_devices,
"rf_signals": report.rf_signals,
"new_devices": report.new_devices,
"missing_devices": report.missing_devices,
"high_interest_count": len(report.high_interest_findings),
"needs_review_count": len(report.needs_review_findings),
"informational_count": len(report.informational_findings),
},
"findings": {
"high_interest": [
{
"identifier": f.identifier,
"protocol": f.protocol,
"name": f.name,
"risk_score": f.risk_score,
"description": f.description,
"indicators": f.indicators,
"recommended_action": f.recommended_action,
"signal_classification": {
"strength": f.signal_strength,
"confidence": f.signal_confidence,
"interpretation": f.signal_interpretation,
"caveats": f.signal_caveats,
},
}
for f in report.high_interest_findings
],
"needs_review": [
{
"identifier": f.identifier,
"protocol": f.protocol,
"name": f.name,
"risk_score": f.risk_score,
"description": f.description,
"indicators": f.indicators,
"signal_classification": {
"strength": f.signal_strength,
"confidence": f.signal_confidence,
"interpretation": f.signal_interpretation,
"caveats": f.signal_caveats,
},
}
for f in report.needs_review_findings
],
},
"meeting_windows": [
{
"name": m.name,
"start_time": m.start_time,
"end_time": m.end_time,
"duration_minutes": m.duration_minutes,
"devices_first_seen": m.devices_first_seen,
"behavior_changes": m.behavior_changes,
"high_interest_devices": m.high_interest_devices,
}
for m in report.meeting_summaries
],
"device_timelines": report.device_timelines,
"all_indicators": report.all_indicators,
"baseline_diff": report.baseline_diff,
"correlations": report.correlation_data,
}
def generate_technical_annex_csv(report: TSCMReport) -> str:
"""
Generate device timeline data as CSV.
Provides spreadsheet-compatible format for further analysis.
"""
output = io.StringIO()
writer = csv.writer(output)
# Header
writer.writerow(
[
"identifier",
"protocol",
"name",
"risk_level",
"risk_score",
"first_seen",
"last_seen",
"observation_count",
"rssi_min",
"rssi_max",
"rssi_mean",
"rssi_stability",
"movement_pattern",
"meeting_correlated",
"indicators",
]
)
# Device data from timelines
for timeline in report.device_timelines:
indicators_str = "; ".join(f"{i.get('type', '')}({i.get('score', 0)})" for i in timeline.get("indicators", []))
signal = timeline.get("signal", {})
metrics = timeline.get("metrics", {})
movement = timeline.get("movement", {})
meeting = timeline.get("meeting_correlation", {})
writer.writerow(
[
timeline.get("identifier", ""),
timeline.get("protocol", ""),
timeline.get("name", ""),
timeline.get("risk_level", "informational"),
timeline.get("risk_score", 0),
metrics.get("first_seen", ""),
metrics.get("last_seen", ""),
metrics.get("total_observations", 0),
signal.get("rssi_min", ""),
signal.get("rssi_max", ""),
signal.get("rssi_mean", ""),
signal.get("stability", ""),
movement.get("pattern", ""),
meeting.get("correlated", False),
indicators_str,
]
)
# Also add findings summary
writer.writerow([])
writer.writerow(["--- FINDINGS SUMMARY ---"])
writer.writerow(
[
"identifier",
"protocol",
"risk_level",
"risk_score",
"signal_strength",
"signal_confidence",
"description",
"interpretation",
"recommended_action",
]
)
all_findings = report.high_interest_findings + report.needs_review_findings
for finding in all_findings:
writer.writerow(
[
finding.identifier,
finding.protocol,
finding.risk_level,
finding.risk_score,
finding.signal_strength or "",
finding.signal_confidence or "",
finding.description,
finding.signal_interpretation or "",
finding.recommended_action,
]
)
return output.getvalue()
# =============================================================================
# Report Builder
# =============================================================================
class TSCMReportBuilder:
"""
Builder for constructing TSCM reports from sweep data.
Usage:
builder = TSCMReportBuilder(sweep_id=123)
builder.set_location("Conference Room A")
builder.add_capabilities(capabilities_dict)
builder.add_finding(finding)
report = builder.build()
"""
def __init__(self, sweep_id: int):
self.sweep_id = sweep_id
self.report = TSCMReport(
report_id=f"TSCM-{sweep_id}-{datetime.now().strftime('%Y%m%d%H%M%S')}",
generated_at=datetime.now(),
sweep_id=sweep_id,
sweep_type="standard",
)
def set_sweep_type(self, sweep_type: str) -> TSCMReportBuilder:
self.report.sweep_type = sweep_type
return self
def set_location(self, location: str) -> TSCMReportBuilder:
self.report.location = location
return self
def set_examiner(self, examiner_name: str) -> TSCMReportBuilder:
self.report.examiner_name = examiner_name
return self
def set_baseline(self, baseline_id: int, baseline_name: str) -> TSCMReportBuilder:
self.report.baseline_id = baseline_id
self.report.baseline_name = baseline_name
return self
def set_sweep_times(self, start: datetime, end: datetime | None = None) -> TSCMReportBuilder:
self.report.sweep_start = start
self.report.sweep_end = end or datetime.now()
self.report.duration_minutes = (self.report.sweep_end - self.report.sweep_start).total_seconds() / 60
return self
def add_capabilities(self, capabilities: dict) -> TSCMReportBuilder:
self.report.capabilities = capabilities
self.report.limitations = capabilities.get("all_limitations", [])
return self
def add_finding(self, finding: ReportFinding) -> TSCMReportBuilder:
if finding.risk_level == "high_interest":
self.report.high_interest_findings.append(finding)
elif finding.risk_level in ["review", "needs_review"]:
self.report.needs_review_findings.append(finding)
else:
self.report.informational_findings.append(finding)
return self
def add_findings_from_profiles(self, profiles: list[dict]) -> TSCMReportBuilder:
"""Add findings from correlation engine device profiles."""
for profile in profiles:
# Get signal classification data
signal_data = self._classify_finding_signal(profile)
finding = ReportFinding(
identifier=profile.get("identifier", ""),
protocol=profile.get("protocol", ""),
name=profile.get("name"),
risk_level=profile.get("risk_level", "informational"),
risk_score=profile.get("total_score", 0),
description=self._generate_finding_description(profile),
indicators=profile.get("indicators", []),
recommended_action=profile.get("recommended_action", "monitor"),
playbook_reference=self._get_playbook_reference(profile),
signal_strength=signal_data["signal_strength"],
signal_confidence=signal_data["signal_confidence"],
signal_interpretation=signal_data["signal_interpretation"],
signal_caveats=signal_data["signal_caveats"],
)
self.add_finding(finding)
return self
def _generate_finding_description(self, profile: dict) -> str:
"""Generate description from profile indicators using hedged language."""
indicators = profile.get("indicators", [])
protocol = profile.get("protocol", "Unknown").upper()
# Get signal data for context
rssi = profile.get("rssi_mean") or profile.get("rssi")
duration = profile.get("observation_duration_seconds")
observation_count = profile.get("observation_count", 1)
# Assess signal to determine confidence
assessment = assess_signal(rssi, duration, observation_count)
confidence = assessment.confidence
if not indicators:
# Use hedged language based on confidence
return generate_hedged_statement(f"Observed {protocol} signal", "device_presence", confidence)
# Build description with hedged language
primary = indicators[0]
indicator_type = primary.get("type", "pattern")
# Map indicator types to hedged descriptions
if indicator_type in ("airtag_detected", "tile_detected", "smarttag_detected", "known_tracker"):
desc = generate_hedged_statement(f"{protocol} signal characteristics", "device_presence", confidence)
desc += f" - pattern consistent with {indicator_type.replace('_', ' ')}"
elif indicator_type == "audio_capable":
desc = generate_hedged_statement("Device characteristics", "surveillance_indicator", confidence)
desc += " - audio-capable device type identified"
elif indicator_type in ("hidden_identity", "hidden_ssid"):
desc = generate_hedged_statement("Network configuration", "surveillance_indicator", confidence)
desc += " - concealed identity pattern observed"
else:
desc = generate_hedged_statement(f"{protocol} signal pattern", "device_presence", confidence)
if len(indicators) > 1:
desc += f" (+{len(indicators) - 1} additional indicators)"
return desc
def _classify_finding_signal(self, profile: dict) -> dict:
"""Extract signal classification data for a finding."""
rssi = profile.get("rssi_mean") or profile.get("rssi")
duration = profile.get("observation_duration_seconds")
observation_count = profile.get("observation_count", 1)
assessment = assess_signal(rssi, duration, observation_count)
return {
"signal_strength": assessment.signal_strength.value,
"signal_confidence": assessment.confidence.value,
"signal_interpretation": assessment.interpretation,
"signal_caveats": assessment.caveats,
}
def _get_playbook_reference(self, profile: dict) -> str:
"""Get playbook reference based on profile."""
risk_level = profile.get("risk_level", "informational")
indicators = profile.get("indicators", [])
# Check for tracker
tracker_types = ["airtag_detected", "tile_detected", "smarttag_detected", "known_tracker"]
if any(i.get("type") in tracker_types for i in indicators) and risk_level == "high_interest":
return "PB-001 (Tracker Detection)"
if risk_level == "high_interest":
return "PB-002 (Suspicious Device)"
elif risk_level in ["review", "needs_review"]:
return "PB-003 (Unknown Device)"
return ""
def add_meeting_summary(self, summary: dict) -> TSCMReportBuilder:
"""Add meeting window summary."""
meeting = ReportMeetingSummary(
name=summary.get("name"),
start_time=summary.get("start_time", ""),
end_time=summary.get("end_time"),
duration_minutes=summary.get("duration_minutes", 0),
devices_first_seen=summary.get("devices_first_seen", 0),
behavior_changes=summary.get("behavior_changes", 0),
high_interest_devices=summary.get("high_interest_devices", 0),
)
self.report.meeting_summaries.append(meeting)
return self
def add_statistics(
self, wifi: int = 0, wifi_clients: int = 0, bluetooth: int = 0, rf: int = 0, new: int = 0, missing: int = 0
) -> TSCMReportBuilder:
self.report.wifi_devices = wifi
self.report.wifi_clients = wifi_clients
self.report.bluetooth_devices = bluetooth
self.report.rf_signals = rf
self.report.total_devices_scanned = wifi + wifi_clients + bluetooth + rf
self.report.new_devices = new
self.report.missing_devices = missing
return self
def add_device_timelines(self, timelines: list[dict]) -> TSCMReportBuilder:
self.report.device_timelines = timelines
return self
def add_all_indicators(self, indicators: list[dict]) -> TSCMReportBuilder:
self.report.all_indicators = indicators
return self
def add_baseline_diff(self, diff: dict) -> TSCMReportBuilder:
self.report.baseline_diff = diff
return self
def add_correlations(self, correlations: list[dict]) -> TSCMReportBuilder:
self.report.correlation_data = correlations
return self
def build(self) -> TSCMReport:
"""Build and return the complete report."""
# Calculate overall risk assessment
if self.report.high_interest_findings:
if len(self.report.high_interest_findings) >= 3:
self.report.overall_risk_assessment = "high"
else:
self.report.overall_risk_assessment = "elevated"
elif self.report.needs_review_findings:
self.report.overall_risk_assessment = "moderate"
else:
self.report.overall_risk_assessment = "low"
self.report.key_findings_count = len(self.report.high_interest_findings) + len(
self.report.needs_review_findings
)
# Generate executive summary
self.report.executive_summary = generate_executive_summary(self.report)
return self.report
# =============================================================================
# Report Generation API Functions
# =============================================================================
def generate_report(
sweep_id: int,
sweep_data: dict,
device_profiles: list[dict],
capabilities: dict,
timelines: list[dict],
baseline_diff: dict | None = None,
meeting_summaries: list[dict] | None = None,
correlations: list[dict] | None = None,
categories: list[str] | None = None,
site_name: str = "",
examiner_name: str = "",
) -> TSCMReport:
"""
Generate a complete TSCM report from sweep data.
Args:
sweep_id: Sweep ID
sweep_data: Sweep dict from database
device_profiles: List of DeviceProfile dicts from correlation engine
capabilities: Capabilities dict
timelines: Device timeline dicts
baseline_diff: Optional baseline diff dict
meeting_summaries: Optional meeting summaries
correlations: Optional correlation data
Returns:
Complete TSCMReport
"""
builder = TSCMReportBuilder(sweep_id)
# Basic info
builder.set_sweep_type(sweep_data.get("sweep_type", "standard"))
if site_name:
builder.set_location(site_name)
if examiner_name:
builder.set_examiner(examiner_name)
# Parse times
started_at = sweep_data.get("started_at")
completed_at = sweep_data.get("completed_at")
if started_at:
if isinstance(started_at, str):
started_at = datetime.fromisoformat(started_at.replace("Z", "+00:00")).replace(tzinfo=None)
if completed_at and isinstance(completed_at, str):
completed_at = datetime.fromisoformat(completed_at.replace("Z", "+00:00")).replace(tzinfo=None)
builder.set_sweep_times(started_at, completed_at)
# Capabilities
builder.add_capabilities(capabilities)
# Apply category filter before building findings
if categories:
_cat_map = {"needs_review": {"review", "needs_review"}}
allowed = set()
for c in categories:
allowed |= _cat_map.get(c, {c})
device_profiles = [p for p in device_profiles if p.get("risk_level", "informational") in allowed]
# Add findings from profiles
builder.add_findings_from_profiles(device_profiles)
# Statistics
results = sweep_data.get("results", {})
wifi_count = results.get("wifi_count")
if wifi_count is None:
wifi_count = len(results.get("wifi_devices", results.get("wifi", [])))
wifi_client_count = results.get("wifi_client_count")
if wifi_client_count is None:
wifi_client_count = len(results.get("wifi_clients", []))
bt_count = results.get("bt_count")
if bt_count is None:
bt_count = len(results.get("bt_devices", results.get("bluetooth", [])))
rf_count = results.get("rf_count")
if rf_count is None:
rf_count = len(results.get("rf_signals", results.get("rf", [])))
builder.add_statistics(
wifi=wifi_count,
wifi_clients=wifi_client_count,
bluetooth=bt_count,
rf=rf_count,
new=baseline_diff.get("summary", {}).get("new_devices", 0) if baseline_diff else 0,
missing=baseline_diff.get("summary", {}).get("missing_devices", 0) if baseline_diff else 0,
)
# Technical data
builder.add_device_timelines(timelines)
if baseline_diff:
builder.add_baseline_diff(baseline_diff)
if meeting_summaries:
for summary in meeting_summaries:
builder.add_meeting_summary(summary)
if correlations:
builder.add_correlations(correlations)
# Extract all indicators
all_indicators = []
for profile in device_profiles:
for ind in profile.get("indicators", []):
all_indicators.append({"device": profile.get("identifier"), "protocol": profile.get("protocol"), **ind})
builder.add_all_indicators(all_indicators)
return builder.build()
def get_pdf_report(report: TSCMReport) -> str:
"""Get PDF-ready report content."""
return generate_pdf_content(report)
def get_json_annex(report: TSCMReport) -> dict:
"""Get JSON technical annex."""
return generate_technical_annex_json(report)
def get_csv_annex(report: TSCMReport) -> str:
"""Get CSV technical annex."""
return generate_technical_annex_csv(report)
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