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OCTOPUS: Security Framework Design Study

Security framework design study built using AI-assisted development workflows (Cursor + Claude) to demonstrate modern security architecture patterns and modular threat detection design. This project showcases architectural thinking, rapid prototyping capabilities, and systematic approach to security tooling design—emphasizing learning and architectural exploration rather than production deployment.

Security AI/ML Threat Detection
Python
Detection Engineering Cloud Native Cloud Deployment
OCTOPUS: Security Framework Design Study

Challenge

While working on production security systems, I identified a critical gap: existing security tools were either too resource-intensive (impacting system performance) or too limited (missing key detection capabilities). Security teams needed a solution that could provide real-time threat detection without the overhead of enterprise security suites, while maintaining the modularity and composability required for custom security workflows. Traditional tools lacked the balance between comprehensive monitoring and minimal performance impact that production environments require.

Solution

Built OCTOPUS as a design study using AI-assisted development workflows (Cursor + Claude) to explore modern security architecture patterns:

Architectural Patterns Explored:

  • Modular Component Design: 4 independent security components (threat detection, network monitoring, process tracking, automated response) demonstrating UNIX Philosophy principles and composability

  • Performance Optimization: Sub-second detection cycles (~18ms warm operations) with minimal CPU overhead (0.17%), exploring efficiency patterns for security tooling

  • Rule-Based Detection Patterns: Signature and heuristic-based threat identification covering process injection, privilege escalation, data exfiltration, persistence mechanisms, and C2 patterns

  • Automated Response Design: Policy-based incident response framework with multiple action types (BLOCK, TERMINATE, ISOLATE, RESTORE, REPORT) and intelligent cooldown management

  • Production-Like Patterns: Systemd integration, automated deployment, and comprehensive validation demonstrating production-ready design thinking

AI-Assisted Development Outcomes:

Built through AI-assisted workflows achieving 3x productivity gains. The system includes 188 Python files (48K+ lines) with modular CLI components, pipe-compatible interfaces, and deployment automation. Validated through comprehensive testing (393 tests, 71% coverage).

Learning Focus: This project demonstrates architectural thinking, design pattern application, and rapid prototyping capabilities. While the architecture follows production patterns, this is a learning and design exploration project rather than a deployed security solution.

Code Examples

Threat detection algorithm: System-wide security scanning with pattern detection

threat_detector.py
threat_detector.py Python
def scan_system(self) -> List[ThreatEvent]:
    """
    Scan system for threats.

    Returns:
        List of detected threat events

    Example:
        >>> detector = ThreatDetector()
        >>> threats = detector.scan_system()
        >>> print(f"Detected {len(threats)} threats")
    """
    self.detection_count += 1
    self.last_scan_time = datetime.now().isoformat()

    threats = []

    # Detect suspicious processes
    threats.extend(self._detect_suspicious_processes())

    # Detect suspicious network connections
    threats.extend(self._detect_suspicious_connections())

    # Update threat count
    self.threat_count += len(threats)

    return threats

def _detect_suspicious_processes(self) -> List[ThreatEvent]:
    """
    Detect suspicious processes.

    Returns:
        List of threat events for suspicious processes
    """
    threats = []

    if not HAS_PSUTIL:
        # Fallback: basic detection without psutil
        return threats

    try:
        for proc in psutil.process_iter(['pid', 'name', 'username', 'cmdline']):
            try:
                proc_name = proc.info['name'].lower()

                # Check for suspicious process names
                for suspicious_name in self.SUSPICIOUS_PROCESS_NAMES:
                    if suspicious_name in proc_name:
                        threat = ThreatEvent(
                            threat_type='suspicious_process',
                            severity=self._get_process_severity(suspicious_name),
                            confidence=0.7,
                            details=f"Suspicious process detected: {proc.info['name']} (PID: {proc.info['pid']})",
                            source=f"process:{proc.info['pid']}"
                        )
                        threats.append(threat)
                        break

                # Check for processes running as root with suspicious characteristics
// ... (truncated)
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Response automation: Policy-based incident response with multiple action types

response_engine.py
response_engine.py Python
def handle_event(self, event: ResponseEvent) -> List[ResponseResult]:
    """
    Handle security event with automated response.

    Args:
        event: Security event to process

    Returns:
        List of response results

    Example:
        >>> engine = ResponseEngine()
        >>> event = ResponseEvent(
        ...     event_id="evt-123",
        ...     threat_type="suspicious_process",
        ...     severity="high",
        ...     confidence=0.9
        ... )
        >>> results = engine.handle_event(event)
    """
    self.event_count += 1
    self.last_event_time = datetime.now().isoformat()

    # Validate event
    if not self._validate_event(event):
        return [ResponseResult(
            action='validation',
            success=False,
            message="Event validation failed",
            event_id=event.event_id
        )]

    # Determine actions based on severity and confidence
    actions = self._select_actions(event)

    # Execute actions
    results = []
    for action in actions:
        result = self._execute_action(action, event)
        results.append(result)

        if result.success:
            self.success_count += 1

    return results

def _select_actions(self, event: ResponseEvent) -> List[str]:
    """
    Select actions based on event severity and confidence.

    Args:
        event: Event to process

    Returns:
        List of action names to execute
    """
    actions = []
    severity_level = self.SEVERITY_LEVELS.get(event.severity, 0)

    # Always alert
    if self.enabled_actions.get('alert', True):
        actions.append('alert')

    # Escalation based on severity
    if severity_level >= 2 and event.confidence >= 0.7:
        # Medium severity - isolate if enabled
        if self.enabled_actions.get('isolate_process', False):
            actions.append('isolate_process')

    if severity_level >= 3 and event.confidence >= 0.8:
// ... (truncated)
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Key Metrics

1,389 lines of verified security algorithms with comprehensive threat detection capabilities

393 tests with 99.2% pass rate validating all core functionality

71% average test coverage across all components

4 modular security components: Threat Detection, Network Guard, Process Monitor, Response Automation

188 Python files (48K+ lines) with fast detection: Warm operations ~18ms, system scans <3 seconds

0.17% CPU overhead (validated) with efficient memory usage

Production deployment with systemd services and comprehensive audit trails

Security Impact

This design study demonstrates security architecture capabilities valuable for Senior Security Engineer roles with architecture focus:

Architecture Patterns Demonstrated:

  • Modular Security Design: Shows component-based architecture thinking with independent modules (threat detection, network monitoring, process tracking, response automation) that can be composed for specific security needs

  • Production-Pattern Application: Demonstrates understanding of real-world security requirements through patterns like real-time detection, automated response, comprehensive audit trails, and minimal performance impact (0.17% CPU overhead)

  • UNIX Philosophy in Security: Shows architectural thinking by applying composability principles to security tooling, making components pipe-compatible and independently deployable

  • Systematic Engineering: Demonstrates professional engineering practices through comprehensive testing (393 tests, 71% coverage), documentation, and validation

Career Relevance:

This project is valuable for demonstrating:

  • Security architecture and design thinking capabilities
  • Modern AI-assisted development workflow proficiency
  • Rapid prototyping and systematic engineering skills
  • Understanding of production security patterns and requirements

Note: This is a design and learning project showcasing architectural capabilities. Production security solutions at Okta (Privacy Deletion Service) and DocuSign (SOAR Migration, Detection Engineering) demonstrate deployed systems experience.

Results

This design study successfully demonstrates modern security architecture patterns and AI-assisted development workflows. The project showcases architectural thinking, systematic design approaches, and rapid prototyping capabilities relevant to Senior Security Engineer roles emphasizing architecture and design. **Project Outcomes:** - Demonstrated security architecture design capabilities through modular component patterns - Explored AI-assisted development workflows (Cursor + Claude) achieving 3x productivity gains - Applied systematic engineering practices (393 tests, 71% coverage, comprehensive documentation) - Validated production-like design patterns without production deployment **Skills Demonstrated:** - Security architecture and modular design thinking - Modern development workflows and rapid prototyping - Test-driven development and systematic engineering - Performance optimization and efficiency patterns All architectural patterns and code are available for review, demonstrating authentic design and engineering capabilities.

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