50 Real-World Use Cases for Wazuh MCP Server: From SOC to DevSecOps#

1
async def automated_alert_triage(alert):
2
    """AI-powered alert triage with context enrichment"""
3

4
    triage_query = f"""
5
    Analyze this security alert and provide:
6
    1. True positive probability (0-100%)
7
    2. MITRE ATT&CK mapping
8
    3. Affected business assets
9
    4. Similar historical incidents
10
    5. Recommended response priority
11

12
    Alert: {alert}
13
    """
14

15
    result = await mcp_agent.query(triage_query)
16

17
    if result['true_positive_probability'] > 80:
18
        await escalate_to_tier2(alert, result)
19
    elif result['true_positive_probability'] > 50:
20
        await assign_to_tier1(alert, result)
21
    else:
22
        await mark_as_false_positive(alert, result)

1
async def generate_shift_handover():
2
    """Generate comprehensive shift handover report"""
3

4
    handover_query = """
5
    Generate shift handover report for the last 8 hours:
6
    1. Critical incidents and their current status
7
    2. Ongoing investigations with next steps
8
    3. Systems under monitoring
9
    4. Pending actions requiring follow-up
10
    5. Unusual patterns observed
11
    6. Resource utilization and performance metrics
12
    """
13

14
    report = await mcp_agent.query(handover_query)
15
    return format_handover_report(report)

1
async def correlate_with_threat_intel(indicator):
2
    """Correlate indicators with threat intelligence"""
3

4
    correlation_query = f"""
5
    Check if {indicator} appears in any Wazuh agents:
6
    1. Search for the indicator in network connections
7
    2. Check process command lines
8
    3. Look in file hashes
9
    4. Search DNS queries
10
    5. Check registry entries (Windows)
11

12
    Provide:
13
    - Affected agents
14
    - First seen timestamp
15
    - Related activities
16
    - Confidence score
17
    """
18

19
    matches = await mcp_agent.query(correlation_query)
20

21
    if matches['affected_agents']:
22
        await create_incident_from_ioc(indicator, matches)

1
class PlaybookExecutor:
2
    async def execute_ransomware_playbook(self, agent_id):
3
        """Execute ransomware response playbook"""
4

5
        steps = [
6
            "Isolate the affected agent from network",
7
            "Identify ransomware process and kill it",
8
            "Check for lateral movement to other agents",
9
            "Identify encrypted files and backup status",
10
            "Collect forensic artifacts",
11
            "Check for data exfiltration indicators",
12
            "Generate incident report with timeline"
13
        ]
14

15
        results = []
16
        for step in steps:
17
            result = await mcp_agent.query(f"{step} for agent {agent_id}")
18
            results.append(result)
19

20
            if "critical" in result.lower():
21
                await escalate_to_management(results)
22

23
        return compile_playbook_results(results)

1
async def generate_security_dashboard():
2
    """Generate real-time security posture metrics"""
3

4
    metrics_queries = {
5
        "agent_health": "Count agents by status (active, disconnected, pending)",
6
        "critical_vulns": "List agents with critical vulnerabilities",
7
        "compliance_score": "Calculate overall compliance percentage",
8
        "threat_level": "Assess current threat level based on recent alerts",
9
        "patch_status": "Show patch compliance across all systems",
10
        "suspicious_activity": "Identify top 5 suspicious activities in last hour"
11
    }
12

13
    dashboard_data = {}
14
    for metric, query in metrics_queries.items():
15
        dashboard_data[metric] = await mcp_agent.query(query)
16

17
    return render_dashboard(dashboard_data)

1
async def analyze_user_behavior(username):
2
    """Analyze user behavior for anomalies"""
3

4
    behavior_query = f"""
5
    Analyze behavior for user {username} over the last 7 days:
6
    1. Login patterns (time, location, frequency)
7
    2. Accessed resources and data volumes
8
    3. Executed commands and processes
9
    4. Network connections established
10
    5. File operations (especially on sensitive data)
11

12
    Compare with baseline and identify:
13
    - Deviation score from normal behavior
14
    - Suspicious activities
15
    - Risk indicators
16
    """
17

18
    analysis = await mcp_agent.query(behavior_query)
19

20
    if analysis['deviation_score'] > 75:
21
        await trigger_user_investigation(username, analysis)

1
async def ioc_sweep(ioc_list):
2
    """Sweep infrastructure for indicators of compromise"""
3

4
    sweep_results = []
5

6
    for ioc in ioc_list:
7
        sweep_query = f"""
8
        Search all agents for IOC: {ioc['value']}
9
        Type: {ioc['type']}
10

11
        Check in:
12
        - Running processes
13
        - Network connections
14
        - File system
15
        - Registry (Windows)
16
        - Scheduled tasks
17
        - Service configurations
18

19
        Return: agent_id, location_found, context
20
        """
21

22
        result = await mcp_agent.query(sweep_query)
23

24
        if result['found']:
25
            sweep_results.append({
26
                'ioc': ioc,
27
                'findings': result['findings'],
28
                'severity': calculate_severity(result)
29
            })
30

31
    return generate_ioc_sweep_report(sweep_results)

1
async def capacity_planning_analysis():
2
    """Analyze capacity trends and predict future needs"""
3

4
    capacity_query = """
5
    Analyze system capacity over the last 30 days:
6
    1. Agent count growth rate
7
    2. Event volume trends
8
    3. Storage consumption rate
9
    4. Processing time patterns
10
    5. Network bandwidth utilization
11

12
    Predict for next 90 days:
13
    - Expected agent count
14
    - Storage requirements
15
    - Processing capacity needs
16
    - Recommended infrastructure changes
17
    """
18

19
    analysis = await mcp_agent.query(capacity_query)
20
    return generate_capacity_report(analysis)

1
async def generate_executive_metrics():
2
    """Generate executive security metrics"""
3

4
    metrics_query = """
5
    Generate executive security report for last month:
6

7
    KPIs:
8
    - Mean time to detect (MTTD)
9
    - Mean time to respond (MTTR)
10
    - Alert volume and trends
11
    - False positive rate
12
    - Coverage gaps identified
13

14
    Incidents:
15
    - Total incidents by severity
16
    - Resolution status
17
    - Root causes
18

19
    Compliance:
20
    - Compliance score by framework
21
    - Failed controls
22
    - Remediation progress
23
    """
24

25
    metrics = await mcp_agent.query(metrics_query)
26
    return format_executive_report(metrics)

1
async def daily_threat_briefing():
2
    """Generate daily threat intelligence briefing"""
3

4
    briefing_query = """
5
    Generate threat briefing for the last 24 hours:
6

7
    1. New threats detected in environment
8
    2. Suspicious patterns identified
9
    3. Failed attack attempts
10
    4. Vulnerability discoveries
11
    5. External threat landscape changes
12
    6. Recommended defensive actions
13

14
    Prioritize by business impact
15
    """
16

17
    briefing = await mcp_agent.query(briefing_query)
18
    await distribute_threat_briefing(briefing)

1
async def ransomware_response(infected_agent):
2
    """Comprehensive ransomware incident response"""
3

4
    response_queries = [
5
        f"Immediately isolate agent {infected_agent} from network",
6
        f"Identify ransomware process on {infected_agent} and terminate",
7
        f"Find all files modified in last hour on {infected_agent}",
8
        f"Check for lateral movement from {infected_agent}",
9
        f"Identify data exfiltration from {infected_agent}",
10
        f"List all network connections from {infected_agent} in last 24h",
11
        f"Check for persistence mechanisms on {infected_agent}"
12
    ]
13

14
    response_actions = []
15
    for query in response_queries:
16
        result = await mcp_agent.query(query)
17
        response_actions.append(result)
18

19
        # Check for spread
20
        if "lateral_movement" in str(result):
21
            await initiate_containment_protocol(result)
22

23
    return compile_incident_report(response_actions)

1
async def investigate_data_breach(breach_indicator):
2
    """Investigate potential data breach"""
3

4
    investigation_query = f"""
5
    Investigate data breach related to {breach_indicator}:
6

7
    1. Identify all agents that accessed sensitive data repositories
8
    2. Find unusual data transfer patterns (size, destination, time)
9
    3. Check for privilege escalation before data access
10
    4. Identify staging directories used
11
    5. Analyze compression/encryption of data before transfer
12
    6. Timeline of all related events
13
    7. Identify potential exfiltration methods used
14

15
    Focus on last 30 days of activity
16
    """
17

18
    investigation = await mcp_agent.query(investigation_query)
19

20
    # Determine breach scope
21
    breach_scope = await determine_breach_scope(investigation)
22

23
    # Generate breach notification requirements
24
    notification_requirements = await check_compliance_requirements(breach_scope)
25

26
    return {
27
        'investigation': investigation,
28
        'scope': breach_scope,
29
        'notifications_required': notification_requirements
30
    }

1
async def contain_malware_outbreak(patient_zero):
2
    """Contain malware outbreak from patient zero"""
3

4
    containment_strategy = f"""
5
    Starting from agent {patient_zero}, execute containment:
6

7
    1. Map infection spread pattern
8
    2. Identify all agents with similar IOCs
9
    3. Isolate infected agents progressively
10
    4. Identify and block C2 communications
11
    5. Deploy signatures to uninfected agents
12
    6. Monitor for new infections
13
    7. Validate containment success
14
    """
15

16
    containment_result = await mcp_agent.query(containment_strategy)
17

18
    # Deploy countermeasures
19
    await deploy_malware_signatures(containment_result['iocs'])
20

21
    # Monitor for reinfection
22
    await setup_continuous_monitoring(containment_result['affected_agents'])
23

24
    return containment_result

1
async def apt_hunt_operation():
2
    """Hunt for Advanced Persistent Threats"""
3

4
    apt_hunt_query = """
5
    Hunt for APT indicators across all agents:
6

7
    Techniques to check:
8
    1. Living off the land binaries (LOLBins) abuse
9
    2. Uncommon process trees
10
    3. Memory-only malware indicators
11
    4. Covert channels (DNS tunneling, steganography)
12
    5. Timestomping and anti-forensics
13
    6. Unusual service installations
14
    7. Registry persistence mechanisms
15
    8. WMI event subscriptions
16

17
    Analyze patterns over 90 days for:
18
    - Low and slow exfiltration
19
    - Beacon patterns
20
    - Lateral movement chains
21
    """
22

23
    apt_indicators = await mcp_agent.query(apt_hunt_query)
24

25
    # Deep dive on suspicious findings
26
    for indicator in apt_indicators['suspicious_patterns']:
27
        await deep_forensic_analysis(indicator)
28

29
    return generate_apt_hunt_report(apt_indicators)

1
async def reconstruct_attack_timeline(incident_id):
2
    """Reconstruct complete attack timeline"""
3

4
    timeline_query = f"""
5
    Reconstruct timeline for incident {incident_id}:
6

7
    1. Initial compromise vector and time
8
    2. Privilege escalation events
9
    3. Lateral movement sequence
10
    4. Data access and staging
11
    5. Exfiltration attempts
12
    6. Persistence establishment
13
    7. Cleanup attempts
14

15
    Correlate:
16
    - Process creation events
17
    - Network connections
18
    - File modifications
19
    - Registry changes
20
    - Authentication events
21

22
    Output: Chronological event sequence with evidence
23
    """
24

25
    timeline = await mcp_agent.query(timeline_query)
26

27
    # Generate forensic report
28
    forensic_report = create_forensic_timeline(timeline)
29

30
    # Identify gaps in visibility
31
    visibility_gaps = identify_monitoring_gaps(timeline)
32

33
    return {
34
        'timeline': forensic_report,
35
        'visibility_gaps': visibility_gaps,
36
        'evidence_preserved': timeline['evidence']
37
    }

1
async def detect_insider_threat(risk_indicators):
2
    """Detect potential insider threats"""
3

4
    insider_detection_query = """
5
    Analyze for insider threat indicators:
6

7
    User behavior analysis:
8
    1. After-hours access to sensitive systems
9
    2. Bulk data downloads
10
    3. Access to systems outside job role
11
    4. Use of unauthorized tools
12
    5. Attempts to bypass security controls
13
    6. Suspicious email patterns
14

15
    System analysis:
16
    1. Unauthorized privilege changes
17
    2. Audit log tampering
18
    3. Unusual peripheral device usage
19
    4. Cloud storage uploads
20
    5. Personal email usage on corporate systems
21

22
    Risk scoring based on:
23
    - Frequency of suspicious activities
24
    - Sensitivity of accessed data
25
    - Deviation from peer group behavior
26
    """
27

28
    threat_analysis = await mcp_agent.query(insider_detection_query)
29

30
    high_risk_users = filter_high_risk(threat_analysis)
31

32
    for user in high_risk_users:
33
        await initiate_user_monitoring(user)
34
        await notify_hr_security(user)
35

36
    return threat_analysis

1
async def zero_day_response(anomaly_pattern):
2
    """Respond to potential zero-day exploit"""
3

4
    zero_day_query = f"""
5
    Investigate potential zero-day based on pattern: {anomaly_pattern}
6

7
    1. Identify all agents showing similar anomalies
8
    2. Analyze process behavior before/after anomaly
9
    3. Check for unusual memory patterns
10
    4. Identify network callbacks
11
    5. Look for privilege escalation
12
    6. Check for code injection indicators
13
    7. Analyze file system changes
14

15
    Create behavioral signature for detection
16
    """
17

18
    analysis = await mcp_agent.query(zero_day_query)
19

20
    # Create custom detection rule
21
    detection_rule = await create_behavioral_rule(analysis)
22

23
    # Deploy protective measures
24
    await deploy_zero_day_mitigation(analysis['affected_agents'])
25

26
    # Share threat intelligence
27
    await share_threat_intelligence(analysis)
28

29
    return analysis

1
async def detect_supply_chain_attack(software_name):
2
    """Detect supply chain compromise"""
3

4
    supply_chain_query = f"""
5
    Investigate potential supply chain attack for {software_name}:
6

7
    1. List all agents with {software_name} installed
8
    2. Check for unusual behavior after installation/update
9
    3. Compare file hashes with known good versions
10
    4. Analyze network connections from the software
11
    5. Check for unexpected child processes
12
    6. Look for registry/file modifications
13
    7. Identify any backdoor indicators
14

15
    Timeline analysis:
16
    - When was software installed/updated
17
    - What changed after installation
18
    - Correlation with security events
19
    """
20

21
    investigation = await mcp_agent.query(supply_chain_query)
22

23
    if investigation['compromise_indicators']:
24
        await quarantine_affected_systems(investigation['affected_agents'])
25
        await notify_software_vendor(software_name, investigation)
26

27
    return investigation

1
async def track_privilege_escalation():
2
    """Track privilege escalation across infrastructure"""
3

4
    privesc_tracking_query = """
5
    Monitor privilege escalation attempts:
6

7
    Windows:
8
    1. UAC bypass attempts
9
    2. Token manipulation
10
    3. Service privilege changes
11
    4. Scheduled task abuse
12
    5. DLL hijacking indicators
13

14
    Linux:
15
    1. Sudo configuration changes
16
    2. SUID/SGID binary execution
17
    3. Kernel exploit indicators
18
    4. Container escapes
19
    5. Capability abuse
20

21
    Track:
22
    - Failed then successful attempts
23
    - Unusual privilege patterns
24
    - Service account abuse
25
    """
26

27
    escalations = await mcp_agent.query(privesc_tracking_query)
28

29
    for escalation in escalations['detected']:
30
        severity = assess_escalation_severity(escalation)
31
        if severity == 'critical':
32
            await immediate_response(escalation)
33
        else:
34
            await log_for_investigation(escalation)
35

36
    return escalations

1
async def assess_incident_impact(incident_data):
2
    """Assess business impact of security incident"""
3

4
    impact_query = f"""
5
    Assess impact of incident: {incident_data}
6

7
    Determine:
8
    1. Affected business services
9
    2. Number of users impacted
10
    3. Data sensitivity exposed
11
    4. Regulatory compliance implications
12
    5. Financial exposure estimate
13
    6. Reputation risk assessment
14
    7. Recovery time estimate
15

16
    Map to:
17
    - Business continuity plans
18
    - Critical asset inventory
19
    - Data classification
20
    """
21

22
    impact_assessment = await mcp_agent.query(impact_query)
23

24
    # Generate executive summary
25
    executive_summary = create_impact_summary(impact_assessment)
26

27
    # Determine notification requirements
28
    notifications = determine_notifications(impact_assessment)
29

30
    return {
31
        'assessment': impact_assessment,
32
        'executive_summary': executive_summary,
33
        'required_notifications': notifications
34
    }

1
async def hunt_behavioral_anomalies():
2
    """Hunt based on behavioral anomalies"""
3

4
    anomaly_hunt = """
5
    Hunt for behavioral anomalies indicating compromise:
6

7
    Process anomalies:
8
    - Rare process executions
9
    - Unusual parent-child relationships
10
    - Processes with no visible window
11
    - High entropy process names
12

13
    Network anomalies:
14
    - Beaconing behavior
15
    - Data transfer to new destinations
16
    - Protocol misuse
17
    - DNS tunneling patterns
18

19
    File system anomalies:
20
    - Mass file encryption
21
    - Unusual file extensions
22
    - Hidden file creation
23
    - Rapid file modifications
24

25
    Score and prioritize findings
26
    """
27

28
    anomalies = await mcp_agent.query(anomaly_hunt)
29

30
    # Apply machine learning for pattern recognition
31
    ml_enhanced = await apply_ml_models(anomalies)
32

33
    return prioritize_findings(ml_enhanced)

1
async def match_threat_actor_ttps(actor_profile):
2
    """Match activities to known threat actor TTPs"""
3

4
    ttp_matching_query = f"""
5
    Match environment activity to threat actor: {actor_profile['name']}
6

7
    Known TTPs:
8
    {actor_profile['techniques']}
9

10
    Search for:
11
    1. Initial access methods used by actor
12
    2. Specific tools and malware families
13
    3. C2 infrastructure patterns
14
    4. Lateral movement techniques
15
    5. Data exfiltration methods
16
    6. Persistence mechanisms
17

18
    Calculate confidence score for actor attribution
19
    """
20

21
    attribution = await mcp_agent.query(ttp_matching_query)
22

23
    if attribution['confidence'] > 70:
24
        await activate_actor_specific_defenses(actor_profile)
25

26
    return attribution

1
async def hunt_memory_threats():
2
    """Hunt for memory-based threats"""
3

4
    memory_hunt_query = """
5
    Hunt for memory-resident threats:
6

7
    1. Process injection indicators
8
    2. Reflective DLL injection
9
    3. Process hollowing
10
    4. Unusual memory allocations
11
    5. RWX memory regions
12
    6. Suspicious threads
13
    7. Hook detection
14
    8. Inline hooks and detours
15

16
    Focus on:
17
    - Critical system processes
18
    - Browser processes
19
    - Security software
20

21
    Analyze memory artifacts for:
22
    - Known malware signatures
23
    - Suspicious strings
24
    - Network indicators
25
    """
26

27
    memory_threats = await mcp_agent.query(memory_hunt_query)
28

29
    for threat in memory_threats['suspicious']:
30
        await capture_memory_dump(threat['agent'], threat['process'])
31
        await submit_for_deep_analysis(threat)
32

33
    return memory_threats

1
async def detect_cloud_service_abuse():
2
    """Detect abuse of legitimate cloud services"""
3

4
    cloud_abuse_query = """
5
    Detect abuse of cloud services for malicious purposes:
6

7
    Services to monitor:
8
    1. Cloud storage (Dropbox, Google Drive, OneDrive)
9
    2. Code repositories (GitHub, GitLab)
10
    3. Collaboration tools (Slack, Teams)
11
    4. Cloud compute (AWS, Azure, GCP)
12
    5. CDNs and hosting
13

14
    Suspicious patterns:
15
    - Large data uploads to personal accounts
16
    - Use of cloud for C2
17
    - Credential storage in cloud
18
    - Automated API abuse
19
    - Shadow IT usage
20

21
    Correlate with:
22
    - User roles and permissions
23
    - Data classification
24
    - Time of activity
25
    """
26

27
    cloud_abuse = await mcp_agent.query(cloud_abuse_query)
28

29
    await enforce_cloud_access_policies(cloud_abuse['violations'])
30

31
    return cloud_abuse

1
async def detect_crypto_mining():
2
    """Detect cryptocurrency mining activities"""
3

4
    mining_detection_query = """
5
    Detect cryptocurrency mining across infrastructure:
6

7
    Indicators:
8
    1. High CPU usage by unknown processes
9
    2. Connections to mining pools
10
    3. Known miner process names/hashes
11
    4. GPU usage patterns
12
    5. Specific network ports (Stratum protocol)
13
    6. Mining-related command lines
14

15
    Check for:
16
    - Browser-based mining scripts
17
    - Container-based miners
18
    - Disguised mining processes
19
    - Mining malware variants
20

21
    Calculate:
22
    - Resource impact
23
    - Estimated costs
24
    - Infection timeline
25
    """
26

27
    mining_activity = await mcp_agent.query(mining_detection_query)
28

29
    for miner in mining_activity['detected']:
30
        await terminate_mining_process(miner)
31
        await block_mining_domains(miner['pools'])
32

33
    return calculate_mining_impact(mining_activity)

1
async def detect_container_escapes():
2
    """Detect container escape attempts"""
3

4
    container_escape_query = """
5
    Monitor for container escape indicators:
6

7
    Techniques:
8
    1. Privileged container abuse
9
    2. Docker socket mounting
10
    3. Kernel exploit attempts
11
    4. Namespace manipulation
12
    5. Cgroup escape attempts
13
    6. Device mounting abuse
14

15
    Suspicious activities:
16
    - Unexpected kernel module loading
17
    - Process running outside expected namespace
18
    - Privilege escalation within container
19
    - Host filesystem access
20
    - Network namespace changes
21

22
    Check container runtime logs and host system
23
    """
24

25
    escapes = await mcp_agent.query(container_escape_query)
26

27
    for escape in escapes['attempts']:
28
        await isolate_container(escape['container_id'])
29
        await patch_vulnerability(escape['exploit_used'])
30

31
    return escapes

1
async def detect_dns_tunneling():
2
    """Detect DNS tunneling for data exfiltration"""
3

4
    dns_tunnel_query = """
5
    Detect DNS tunneling activity:
6

7
    Patterns to identify:
8
    1. High volume of DNS queries to single domain
9
    2. Unusual query types (TXT, NULL, PRIVATE)
10
    3. Long subdomain names
11
    4. High entropy domain names
12
    5. Query/response size anomalies
13
    6. Non-standard DNS ports
14

15
    Analyze:
16
    - Query frequency patterns
17
    - Data encoding in queries
18
    - Response patterns
19
    - Domain reputation
20

21
    Calculate data transfer estimates
22
    """
23

24
    dns_tunneling = await mcp_agent.query(dns_tunnel_query)
25

26
    for tunnel in dns_tunneling['detected']:
27
        await block_dns_tunnel_domain(tunnel['domain'])
28
        await investigate_data_exfiltration(tunnel)
29

30
    return dns_tunneling

1
async def detect_webshells():
2
    """Detect webshells on web servers"""
3

4
    webshell_query = """
5
    Hunt for webshells on all web servers:
6

7
    File indicators:
8
    1. Recently modified web files
9
    2. Files with suspicious functions (eval, exec, system)
10
    3. Obfuscated PHP/ASP/JSP code
11
    4. Files in upload directories
12
    5. Hidden or unusually named files
13

14
    Behavioral indicators:
15
    1. Web process spawning system commands
16
    2. Unusual outbound connections from web service
17
    3. File operations outside webroot
18
    4. Database queries from uploaded files
19

20
    Check common locations:
21
    - Upload directories
22
    - Temp folders
23
    - Image directories
24
    - Plugin/theme folders
25
    """
26

27
    webshells = await mcp_agent.query(webshell_query)
28

29
    for webshell in webshells['found']:
30
        await quarantine_webshell(webshell)
31
        await analyze_webshell_capabilities(webshell)
32
        await check_webshell_usage_logs(webshell)
33

34
    return webshells

1
async def track_lateral_movement():
2
    """Track lateral movement patterns"""
3

4
    lateral_movement_query = """
5
    Track lateral movement across infrastructure:
6

7
    Authentication patterns:
8
    1. Pass-the-hash/ticket attacks
9
    2. Unusual authentication chains
10
    3. Service account abuse
11
    4. RDP/SSH/WinRM connections
12

13
    Techniques:
14
    1. PSExec and variants
15
    2. WMI remote execution
16
    3. Scheduled task creation
17
    4. Service installation
18
    5. Admin share access
19

20
    Map movement:
21
    - Source and destination systems
22
    - Accounts used
23
    - Time patterns
24
    - Data accessed
25

26
    Visualize attack path
27
    """
28

29
    movement_data = await mcp_agent.query(lateral_movement_query)
30

31
    attack_graph = build_attack_graph(movement_data)
32

33
    # Identify and block attack paths
34
    critical_paths = identify_critical_paths(attack_graph)
35
    await block_lateral_movement_paths(critical_paths)
36

37
    return {
38
        'movement_data': movement_data,
39
        'attack_graph': attack_graph,
40
        'blocked_paths': critical_paths
41
    }

1
async def detect_lotl_techniques():
2
    """Detect Living off the Land techniques"""
3

4
    lotl_query = """
5
    Detect Living off the Land (LOTL) techniques:
6

7
    Monitor legitimate tools for abuse:
8

9
    Windows:
10
    - PowerShell encoded commands
11
    - Certutil downloading files
12
    - Bitsadmin transfers
13
    - Rundll32 executing DLLs
14
    - Regsvr32 bypass techniques
15
    - Mshta executing scripts
16

17
    Linux:
18
    - Curl/wget to suspicious domains
19
    - Python/Perl reverse shells
20
    - Netcat backdoors
21
    - Cron job abuse
22
    - Systemd timer abuse
23

24
    Context analysis:
25
    - Parent process relationships
26
    - Command line arguments
27
    - Network connections
28
    - File operations
29
    """
30

31
    lotl_activity = await mcp_agent.query(lotl_query)
32

33
    for activity in lotl_activity['suspicious']:
34
        risk_score = calculate_lotl_risk(activity)
35
        if risk_score > 70:
36
            await investigate_lotl_activity(activity)
37

38
    return lotl_activity

1
async def monitor_pci_compliance():
2
    """Monitor PCI DSS compliance requirements"""
3

4
    pci_query = """
5
    Validate PCI DSS compliance across cardholder environment:
6

7
    Requirements check:
8
    1. Firewall configuration (Req 1)
9
    2. Default passwords changed (Req 2)
10
    3. Cardholder data encryption (Req 3-4)
11
    4. Anti-virus updates (Req 5)
12
    5. Secure development (Req 6)
13
    6. Access control (Req 7-9)
14
    7. Network monitoring (Req 10)
15
    8. Security testing (Req 11)
16
    9. Security policies (Req 12)
17

18
    Generate:
19
    - Compliance percentage
20
    - Failed controls
21
    - Remediation priorities
22
    - Evidence collection
23
    """
24

25
    pci_status = await mcp_agent.query(pci_query)
26

27
    # Generate compliance report
28
    report = generate_pci_report(pci_status)
29

30
    # Auto-remediate where possible
31
    await auto_remediate_pci_failures(pci_status['failures'])
32

33
    return report

1
async def audit_hipaa_security():
2
    """Audit HIPAA security rule compliance"""
3

4
    hipaa_audit_query = """
5
    Audit HIPAA Security Rule compliance:
6

7
    Administrative Safeguards:
8
    1. Access controls and authorization
9
    2. Workforce training records
10
    3. Access audit logs
11
    4. Risk assessments
12

13
    Physical Safeguards:
14
    1. Facility access controls
15
    2. Workstation security
16
    3. Device and media controls
17

18
    Technical Safeguards:
19
    1. Access control systems
20
    2. Audit logs and monitoring
21
    3. Integrity controls
22
    4. Transmission security (encryption)
23

24
    Check for PHI exposure risks
25
    """
26

27
    hipaa_audit = await mcp_agent.query(hipaa_audit_query)
28

29
    # Identify PHI exposure risks
30
    phi_risks = await identify_phi_exposure(hipaa_audit)
31

32
    if phi_risks['critical']:
33
        await immediate_phi_protection(phi_risks)
34

35
    return generate_hipaa_report(hipaa_audit, phi_risks)

1
async def monitor_gdpr_compliance():
2
    """Monitor GDPR data protection compliance"""
3

4
    gdpr_query = """
5
    Monitor GDPR compliance requirements:
6

7
    1. Personal data inventory and mapping
8
    2. Consent management validation
9
    3. Data retention policy enforcement
10
    4. Right to erasure implementation
11
    5. Data portability capabilities
12
    6. Privacy by design validation
13
    7. Data breach detection (<72 hours)
14
    8. Cross-border transfer controls
15
    9. Processor agreement compliance
16
    10. Data protection impact assessments
17

18
    Identify:
19
    - Unauthorized data access
20
    - Data retention violations
21
    - Unencrypted personal data
22
    - Third-party sharing without consent
23
    """
24

25
    gdpr_status = await mcp_agent.query(gdpr_query)
26

27
    # Check for potential breaches
28
    breach_risk = await assess_breach_notification_requirement(gdpr_status)
29

30
    if breach_risk['requires_notification']:
31
        await initiate_gdpr_breach_protocol(breach_risk)
32

33
    return gdpr_status

1
async def soc2_continuous_monitoring():
2
    """Continuous SOC 2 compliance monitoring"""
3

4
    soc2_query = """
5
    Monitor SOC 2 Trust Service Criteria:
6

7
    Security:
8
    - Access controls effectiveness
9
    - System monitoring and alerting
10
    - Incident response procedures
11

12
    Availability:
13
    - System uptime metrics
14
    - Disaster recovery testing
15
    - Performance monitoring
16

17
    Processing Integrity:
18
    - Data validation controls
19
    - Error handling procedures
20

21
    Confidentiality:
22
    - Data encryption status
23
    - Access restrictions
24

25
    Privacy:
26
    - Personal information handling
27
    - Consent management
28

29
    Collect evidence for audit
30
    """
31

32
    soc2_evidence = await mcp_agent.query(soc2_query)
33

34
    # Store evidence for audit
35
    await store_audit_evidence(soc2_evidence)
36

37
    # Identify control gaps
38
    control_gaps = identify_control_gaps(soc2_evidence)
39

40
    return {
41
        'compliance_status': soc2_evidence,
42
        'control_gaps': control_gaps,
43
        'evidence_collected': True
44
    }

1
async def validate_cis_benchmarks():
2
    """Validate CIS benchmark compliance"""
3

4
    cis_validation_query = """
5
    Validate CIS benchmark configurations:
6

7
    Operating Systems:
8
    1. Account policies
9
    2. Local policies
10
    3. Event log settings
11
    4. Restricted groups
12
    5. System services
13
    6. Registry permissions
14
    7. File system permissions
15
    8. Audit policies
16

17
    Applications:
18
    1. Web server hardening
19
    2. Database security
20
    3. Container configurations
21

22
    Score each control:
23
    - Pass/Fail status
24
    - Severity level
25
    - Remediation commands
26
    """
27

28
    cis_results = await mcp_agent.query(cis_validation_query)
29

30
    # Generate remediation scripts
31
    remediation_scripts = generate_cis_remediation(cis_results['failed'])
32

33
    # Apply automatic remediation for low-risk items
34
    await apply_safe_remediations(remediation_scripts)
35

36
    return {
37
        'benchmark_results': cis_results,
38
        'remediation_scripts': remediation_scripts
39
    }

1
async def monitor_iso27001_controls():
2
    """Monitor ISO 27001 control effectiveness"""
3

4
    iso_query = """
5
    Monitor ISO 27001 control effectiveness:
6

7
    Control families:
8
    A.5 - Information security policies
9
    A.6 - Organization of information security
10
    A.7 - Human resource security
11
    A.8 - Asset management
12
    A.9 - Access control
13
    A.10 - Cryptography
14
    A.11 - Physical security
15
    A.12 - Operations security
16
    A.13 - Communications security
17
    A.14 - System development
18
    A.15 - Supplier relationships
19
    A.16 - Incident management
20
    A.17 - Business continuity
21
    A.18 - Compliance
22

23
    Measure control effectiveness and maturity
24
    """
25

26
    iso_controls = await mcp_agent.query(iso_query)
27

28
    # Calculate maturity scores
29
    maturity_scores = calculate_control_maturity(iso_controls)
30

31
    # Identify improvement areas
32
    improvements = identify_iso_improvements(maturity_scores)
33

34
    return {
35
        'control_status': iso_controls,
36
        'maturity_scores': maturity_scores,
37
        'improvement_plan': improvements
38
    }