K7 Total Security Deployment Automation

Skill by ara.so — Security Skills collection.

Overview

K7 Total Security 16.0.1195 is an enterprise-grade endpoint protection framework that provides signatureless AI detection, multi-platform synchronization, and modular protection layers. This skill enables automated deployment, configuration management, and AI-powered threat analysis integration across heterogeneous environments.

Key capabilities:

• Unified security profiles deployable to Windows, macOS, and Linux
• Signatureless behavioral threat detection
• AI-powered incident analysis via OpenAI/Claude APIs
• Multi-layer protection: executable guard, memory scanner, USB filter, web filter
• Centralized policy management and SIEM integration

Installation

Prerequisites

# Ensure administrative/root privileges
# Windows: Run as Administrator
# macOS/Linux: Use sudo

# Verify system requirements
# - Windows 10/11 or Server 2025 (x64, ARM64)
# - macOS 14+ Sonoma/15 Sequoia (Apple Silicon, Intel)
# - Linux Ubuntu 24.04+, Debian 12+, RHEL 9+ (x64, ARM64)

Deploy K7 Console

# Download and install the management console
# Windows
curl -L https://releases.k7security.com/console/k7-console-16.0.1195-win-x64.msi -o k7-console.msi
msiexec /i k7-console.msi /quiet /norestart

# macOS
curl -L https://releases.k7security.com/console/k7-console-16.0.1195-macos.pkg -o k7-console.pkg
sudo installer -pkg k7-console.pkg -target /

# Linux
curl -L https://releases.k7security.com/console/k7-console-16.0.1195-linux-x64.tar.gz -o k7-console.tar.gz
tar -xzf k7-console.tar.gz
sudo ./k7-console/install.sh

# Verify installation
k7-console --version
# Expected: K7 Total Security Console 16.0.1195

Configuration

Basic Profile Structure

K7 uses YAML-based configuration profiles for policy management:

# basic-protection.yaml
profile_name: "Basic Enterprise Protection"
version: "16.0.1195"
enforcement_level: "adaptive"  # Options: strict, adaptive, permissive

protection_layers:
  executable_guard:
    state: enabled
    action_on_threat: "quarantine"  # Options: quarantine, block, log_only
    exceptions:
      - path: "C:\\Program Files\\TrustedApp\\*"
      - path: "/usr/local/bin/safe-script"
  
  memory_scanner:
    state: enabled
    sensitivity: "medium"  # Options: low, medium, high
    scan_interval_seconds: 600
  
  usb_filter:
    state: enabled
    policy: "read_only_for_unknown"  # Options: block_all, read_only_for_unknown, allow_all
    allow_list:
      - vendor_id: "0x0781"  # SanDisk
      - vendor_id: "0x13FE"  # Kingston
  
  web_filter:
    state: enabled
    categories_blocked:
      - "malware_distribution"
      - "phishing"
      - "cryptomining_scripts"
    allow_list:
      - domain: "*.internal.company.com"

logging:
  verbose: true
  retention_days: 90
  forward_to:
    - syslog_server: "${SYSLOG_HOST}:514"
    - elasticsearch: "${ELASTIC_ENDPOINT}"

notifications:
  email:
    enabled: true
    recipients:
      - "${SECURITY_EMAIL}"
  webhook:
    enabled: true
    url: "${SLACK_WEBHOOK_URL}"

AI Integration Configuration

# ai-enhanced-protection.yaml
profile_name: "AI-Enhanced Security"
version: "16.0.1195"
enforcement_level: "adaptive"

protection_layers:
  executable_guard:
    state: enabled
    action_on_threat: "quarantine"
  
  memory_scanner:
    state: enabled
    sensitivity: "high"
    scan_interval_seconds: 300

ai_integration:
  incident_analysis:
    provider: "openai"  # Options: openai, claude
    endpoint: "https://api.openai.com/v1/chat/completions"
    api_key_env: "OPENAI_API_KEY"
    model: "gpt-4-turbo"
    max_tokens: 2000
    temperature: 0.3
    system_prompt: "Analyze security incidents and provide actionable threat intelligence."
  
  automated_response:
    provider: "claude"
    endpoint: "https://api.anthropic.com/v1/messages"
    api_key_env: "ANTHROPIC_API_KEY"
    model: "claude-3-opus-20240229"
    max_tokens: 1500
    webhook_retries: 3
    actions:
      - "generate_incident_report"
      - "suggest_containment_steps"
      - "create_response_script"

threat_intelligence:
  feed_enabled: true
  sources:
    - "https://threat-intel.k7security.com/v1/feeds"
  update_interval_hours: 6

Key Commands

Profile Management

# Apply profile to local endpoint
k7-console --apply-profile ./basic-protection.yaml

# Apply profile to remote endpoint
k7-console --apply-profile ./ai-enhanced-protection.yaml \
  --target 192.168.1.100 \
  --auth-file ./admin-credentials.pem \
  --log-level verbose \
  --timeout 120

# Validate profile syntax without applying
k7-console --validate-profile ./custom-profile.yaml

# List active profiles on endpoint
k7-console --list-profiles --target 192.168.1.100

# Export current configuration
k7-console --export-config --output ./current-config.yaml

Status and Monitoring

# Check protection status
k7-console --status

# View real-time threat events
k7-console --tail-events --filter "severity>=high"

# Generate security report
k7-console --report --format json --output ./security-report.json

# Test AI integration
k7-console --test-ai-integration --provider openai

Bulk Deployment

# Deploy to multiple endpoints from inventory file
k7-console --bulk-deploy \
  --profile ./enterprise-profile.yaml \
  --inventory ./endpoints.txt \
  --parallel 10 \
  --auth-file ./admin-credentials.pem

# endpoints.txt format:
# 192.168.1.100
# 192.168.1.101
# 192.168.1.102

Code Examples

Python: Automated Profile Deployment

import subprocess
import json
import os
from pathlib import Path

class K7SecurityManager:
    def __init__(self, console_path="k7-console", auth_file=None):
        self.console_path = console_path
        self.auth_file = auth_file or os.getenv("K7_AUTH_FILE")
    
    def apply_profile(self, profile_path, target_ip, timeout=120):
        """Apply security profile to remote endpoint."""
        cmd = [
            self.console_path,
            "--apply-profile", profile_path,
            "--target", target_ip,
            "--auth-file", self.auth_file,
            "--log-level", "verbose",
            "--timeout", str(timeout),
            "--output", "json"
        ]
        
        result = subprocess.run(cmd, capture_output=True, text=True)
        
        if result.returncode != 0:
            raise Exception(f"Profile deployment failed: {result.stderr}")
        
        return json.loads(result.stdout)
    
    def get_status(self, target_ip):
        """Retrieve protection status from endpoint."""
        cmd = [
            self.console_path,
            "--status",
            "--target", target_ip,
            "--auth-file", self.auth_file,
            "--output", "json"
        ]
        
        result = subprocess.run(cmd, capture_output=True, text=True)
        return json.loads(result.stdout)
    
    def bulk_deploy(self, profile_path, endpoint_list, parallel=5):
        """Deploy profile to multiple endpoints."""
        # Write endpoints to temporary file
        temp_inventory = Path("/tmp/k7_endpoints.txt")
        temp_inventory.write_text("\n".join(endpoint_list))
        
        cmd = [
            self.console_path,
            "--bulk-deploy",
            "--profile", profile_path,
            "--inventory", str(temp_inventory),
            "--parallel", str(parallel),
            "--auth-file", self.auth_file,
            "--output", "json"
        ]
        
        result = subprocess.run(cmd, capture_output=True, text=True)
        temp_inventory.unlink()
        
        return json.loads(result.stdout)

# Usage
manager = K7SecurityManager(auth_file="./admin-credentials.pem")

# Deploy to single endpoint
response = manager.apply_profile(
    profile_path="./enterprise-profile.yaml",
    target_ip="192.168.1.100"
)
print(f"Deployment status: {response['status']}")
print(f"Active layers: {response['protection_layers_active']}")

# Deploy to multiple endpoints
endpoints = ["192.168.1.100", "192.168.1.101", "192.168.1.102"]
results = manager.bulk_deploy(
    profile_path="./enterprise-profile.yaml",
    endpoint_list=endpoints,
    parallel=3
)
print(f"Successfully deployed to {results['successful_count']} endpoints")

Python: AI-Powered Threat Analysis

import subprocess
import json
import os

class K7AIAnalyzer:
    def __init__(self, console_path="k7-console"):
        self.console_path = console_path
        self.openai_key = os.getenv("OPENAI_API_KEY")
        self.anthropic_key = os.getenv("ANTHROPIC_API_KEY")
    
    def analyze_threat_event(self, event_id, provider="openai"):
        """Request AI analysis of a specific threat event."""
        cmd = [
            self.console_path,
            "--analyze-event", event_id,
            "--ai-provider", provider,
            "--output", "json"
        ]
        
        env = os.environ.copy()
        if provider == "openai":
            env["OPENAI_API_KEY"] = self.openai_key
        elif provider == "claude":
            env["ANTHROPIC_API_KEY"] = self.anthropic_key
        
        result = subprocess.run(cmd, capture_output=True, text=True, env=env)
        return json.loads(result.stdout)
    
    def generate_response_script(self, threat_type, severity):
        """Generate automated response script using AI."""
        cmd = [
            self.console_path,
            "--generate-response",
            "--threat-type", threat_type,
            "--severity", severity,
            "--ai-provider", "claude",
            "--output", "json"
        ]
        
        env = os.environ.copy()
        env["ANTHROPIC_API_KEY"] = self.anthropic_key
        
        result = subprocess.run(cmd, capture_output=True, text=True, env=env)
        response = json.loads(result.stdout)
        return response["script"]

# Usage
analyzer = K7AIAnalyzer()

# Analyze a detected threat
analysis = analyzer.analyze_threat_event(
    event_id="evt_20260312_142301_001",
    provider="openai"
)
print(f"Threat type: {analysis['threat_type']}")
print(f"Severity: {analysis['severity']}")
print(f"Recommended action: {analysis['recommended_action']}")
print(f"AI explanation: {analysis['ai_explanation']}")

# Generate response script
script = analyzer.generate_response_script(
    threat_type="memory_injection",
    severity="high"
)
print("Generated response script:")
print(script)

Bash: Automated Profile Update Script

#!/bin/bash
# update-k7-profiles.sh - Update security profiles across all endpoints

set -e

PROFILE_DIR="./profiles"
INVENTORY_FILE="./endpoints.txt"
AUTH_FILE="${K7_AUTH_FILE:-./admin-credentials.pem}"
LOG_FILE="./deployment-$(date +%Y%m%d-%H%M%S).log"

# Function to deploy profile
deploy_profile() {
    local profile=$1
    local target=$2
    
    echo "[$(date)] Deploying ${profile} to ${target}" | tee -a "$LOG_FILE"
    
    if k7-console --apply-profile "${PROFILE_DIR}/${profile}" \
        --target "$target" \
        --auth-file "$AUTH_FILE" \
        --log-level verbose \
        --timeout 120 >> "$LOG_FILE" 2>&1; then
        echo "[$(date)] ✓ Success: ${target}" | tee -a "$LOG_FILE"
        return 0
    else
        echo "[$(date)] ✗ Failed: ${target}" | tee -a "$LOG_FILE"
        return 1
    fi
}

# Validate profile before deployment
validate_profiles() {
    for profile in "${PROFILE_DIR}"/*.yaml; do
        echo "Validating $(basename "$profile")..."
        if ! k7-console --validate-profile "$profile"; then
            echo "❌ Profile validation failed: $profile"
            exit 1
        fi
    done
    echo "✓ All profiles validated"
}

# Main deployment loop
main() {
    echo "Starting K7 profile deployment at $(date)" | tee "$LOG_FILE"
    
    # Validate profiles
    validate_profiles
    
    # Deploy to each endpoint
    local success_count=0
    local fail_count=0
    
    while IFS= read -r endpoint; do
        # Skip empty lines and comments
        [[ -z "$endpoint" || "$endpoint" =~ ^# ]] && continue
        
        if deploy_profile "enterprise-profile.yaml" "$endpoint"; then
            ((success_count++))
        else
            ((fail_count++))
        fi
    done < "$INVENTORY_FILE"
    
    # Summary
    echo "=================================" | tee -a "$LOG_FILE"
    echo "Deployment Summary:" | tee -a "$LOG_FILE"
    echo "  Successful: $success_count" | tee -a "$LOG_FILE"
    echo "  Failed: $fail_count" | tee -a "$LOG_FILE"
    echo "  Log file: $LOG_FILE" | tee -a "$LOG_FILE"
    echo "=================================" | tee -a "$LOG_FILE"
    
    [[ $fail_count -eq 0 ]] && exit 0 || exit 1
}

main "$@"

Common Patterns

Pattern 1: Environment-Specific Profiles

# production-profile.yaml
profile_name: "Production Environment"
version: "16.0.1195"
enforcement_level: "strict"

protection_layers:
  executable_guard:
    state: enabled
    action_on_threat: "block"  # Strict blocking in production
  memory_scanner:
    state: enabled
    sensitivity: "high"

logging:
  verbose: true
  retention_days: 365  # Long retention for compliance

# development-profile.yaml
profile_name: "Development Environment"
version: "16.0.1195"
enforcement_level: "permissive"

protection_layers:
  executable_guard:
    state: enabled
    action_on_threat: "log_only"  # Non-blocking in dev
    exceptions:
      - path: "C:\\DevWorkspace\\*"
      - path: "/home/*/projects/*"
  memory_scanner:
    state: enabled
    sensitivity: "low"  # Reduce false positives

logging:
  verbose: false
  retention_days: 30

Pattern 2: Role-Based Security Profiles

# admin-workstation-profile.yaml
profile_name: "Administrator Workstation"
version: "16.0.1195"
enforcement_level: "adaptive"

protection_layers:
  executable_guard:
    state: enabled
    action_on_threat: "quarantine"
    exceptions:
      - path: "C:\\AdminTools\\*"
      - hash: "sha256:abc123..."  # Trusted admin tool
  
  usb_filter:
    state: enabled
    policy: "read_only_for_unknown"
    allow_list:
      - vendor_id: "0x0781"
      - serial: "ADMIN_USB_001"

ai_integration:
  incident_analysis:
    provider: "openai"
    model: "gpt-4-turbo"
  automated_response:
    provider: "claude"
    actions:
      - "generate_incident_report"
      - "notify_security_team"

Pattern 3: SIEM Integration

# siem-integrated-profile.yaml
profile_name: "SIEM-Integrated Security"
version: "16.0.1195"
enforcement_level: "adaptive"

protection_layers:
  executable_guard:
    state: enabled
    action_on_threat: "quarantine"
  memory_scanner:
    state: enabled
    sensitivity: "high"

logging:
  verbose: true
  retention_days: 90
  forward_to:
    - syslog_server: "${SYSLOG_HOST}:514"
    - elasticsearch: "${ELASTIC_ENDPOINT}"
    - splunk: "${SPLUNK_HEC_URL}"
  format: "cef"  # Common Event Format for SIEM

threat_intelligence:
  feed_enabled: true
  sources:
    - "${THREAT_INTEL_FEED_URL}"
  update_interval_hours: 6
  
notifications:
  webhook:
    enabled: true
    url: "${SIEM_WEBHOOK_URL}"
    headers:
      Authorization: "Bearer ${SIEM_API_TOKEN}"
    payload_template: |
      {
        "event_type": "{{event_type}}",
        "severity": "{{severity}}",
        "endpoint": "{{endpoint_ip}}",
        "timestamp": "{{timestamp}}",
        "details": "{{details}}"
      }

Troubleshooting

Connection Issues

# Test connectivity to remote endpoint
k7-console --test-connection --target 192.168.1.100

# Verify authentication credentials
k7-console --verify-auth --auth-file ./admin-credentials.pem

# Check firewall rules (port 4451 required)
# Windows
netsh advfirewall firewall show rule name="K7 Management Port"

# Linux
sudo ufw status | grep 4451

Profile Application Failures

# Validate profile syntax
k7-console --validate-profile ./profile.yaml

# Check for conflicting policies
k7-console --check-conflicts --profile ./profile.yaml --target 192.168.1.100

# View detailed error logs
k7-console --apply-profile ./profile.yaml \
  --target 192.168.1.100 \
  --log-level debug \
  --output verbose

AI Integration Issues

# Test OpenAI connectivity
export OPENAI_API_KEY="your-key-here"
k7-console --test-ai-integration --provider openai

# Test Claude connectivity
export ANTHROPIC_API_KEY="your-key-here"
k7-console --test-ai-integration --provider claude

# Check API quota
k7-console --check-ai-quota --provider openai

Performance Optimization

# Optimize for high-performance environments
profile_name: "High-Performance Optimized"
version: "16.0.1195"
enforcement_level: "adaptive"

protection_layers:
  memory_scanner:
    state: enabled
    sensitivity: "medium"
    scan_interval_seconds: 900  # Increase interval
    exclude_processes:
      - "video_encoder.exe"
      - "3d_renderer"
  
  executable_guard:
    state: enabled
    cache_enabled: true  # Enable hash caching
    cache_size_mb: 256

performance_tuning:
  max_cpu_usage_percent: 15
  scan_priority: "low"  # Options: low, normal, high
  io_throttle: true

Common Error Messages

Best Practices

1. Always validate profiles before bulk deployment
2. Use environment variables for sensitive data (API keys, credentials)
3. Test on non-production endpoints before production rollout
4. Monitor AI integration costs — set quotas to prevent runaway API usage
5. Maintain separate profiles for production, staging, and development
6. Enable SIEM forwarding for centralized security monitoring
7. Regularly update threat intelligence feeds (at least daily)
8. Document custom exceptions with business justification
9. Set appropriate retention periods based on compliance requirements
10. Use bulk deployment with parallel processing for large estates