Imported from reason-machines/security-skills (
skills/k7-total-security-deployment-framework/SKILL.md). Install upstream withnpx skills add reason-machines/security-skills --skill k7-total-security-deployment-framework. Copyright stays with the author.
K7 Total Security Deployment Framework
Skill by ara.so — Security Skills collection.
Overview
K7 Total Security 16.0.1195 is a unified endpoint protection framework supporting multi-platform deployment (Windows, macOS, Linux) with AI-powered threat detection, signatureless behavioral analysis, and centralized policy management. This skill covers configuration, deployment automation, and integration patterns.
Installation
Prerequisites
- Administrative access on target endpoints
- Network connectivity to management console (default port 4451)
- Valid K7 Total Security license
- (Optional) OpenAI or Claude API credentials for AI-powered analysis
Console Installation
# Download and install management console
curl -O https://download.k7computing.com/k7-console-16.0.1195.tar.gz
tar -xzf k7-console-16.0.1195.tar.gz
cd k7-console
# Install with default settings
sudo ./install.sh --silent --accept-license
# Verify installation
k7-console --version
# Expected: K7 Total Security Console 16.0.1195
Agent Deployment
# Windows (PowerShell as Administrator)
Invoke-WebRequest -Uri "https://download.k7computing.com/k7-agent-win-16.0.1195.msi" -OutFile "k7-agent.msi"
msiexec /i k7-agent.msi /quiet SERVERURL="https://console.example.com:4451"
# macOS
curl -O https://download.k7computing.com/k7-agent-mac-16.0.1195.pkg
sudo installer -pkg k7-agent-mac-16.0.1195.pkg -target /
# Linux (Debian/Ubuntu)
wget https://download.k7computing.com/k7-agent-linux-16.0.1195.deb
sudo dpkg -i k7-agent-linux-16.0.1195.deb
Core Configuration
Profile Structure
Security profiles are defined in YAML and applied to endpoints via the console or API.
# enterprise_strict.yaml
profile_name: "Enterprise Strict"
version: "16.0.1195"
enforcement_level: "strict"
protection_layers:
executable_guard:
state: enabled
action_on_threat: "block_and_alert"
exceptions:
- path: "/opt/approved-tools/*"
- hash: "sha256:a3f9c82..."
memory_scanner:
state: enabled
sensitivity: "maximum"
scan_interval_seconds: 180
exclude_processes:
- "vmware-vmx"
- "VirtualBox"
usb_filter:
state: enabled
policy: "block_all"
allow_list:
- vendor_id: "0x0781"
product_id: "0x5581"
serial: "ABC123DEF456"
web_filter:
state: enabled
ssl_inspection: true
categories_blocked:
- "malware_distribution"
- "phishing"
- "cryptomining_scripts"
- "peer_to_peer"
allow_list:
- domain: "*.corp.internal"
network_protection:
firewall_mode: "application_aware"
block_outbound_smtp: true
dns_filtering: true
dns_servers:
- "1.1.1.1"
- "8.8.8.8"
ai_integration:
incident_analysis:
provider: "openai"
api_key_env: "K7_OPENAI_API_KEY"
model: "gpt-4-turbo"
max_tokens: 2000
temperature: 0.3
endpoint: "https://api.openai.com/v1/chat/completions"
automated_response:
provider: "claude"
api_key_env: "K7_CLAUDE_API_KEY"
model: "claude-3-opus-20240229"
max_tokens: 1500
endpoint: "https://api.anthropic.com/v1/messages"
logging:
verbose: true
retention_days: 365
forward_to:
- type: "syslog"
host: "siem.example.com"
port: 514
protocol: "tcp"
- type: "elasticsearch"
url: "https://elk.example.com:9200"
index: "k7-security-events"
auth_token_env: "K7_ELK_TOKEN"
notifications:
email:
enabled: true
smtp_server: "smtp.example.com:587"
from: "security@example.com"
recipients:
- "soc@example.com"
tls: true
auth_user_env: "K7_SMTP_USER"
auth_pass_env: "K7_SMTP_PASS"
webhook:
enabled: true
url_env: "K7_WEBHOOK_URL"
method: "POST"
headers:
Content-Type: "application/json"
retry_attempts: 3
timeout_seconds: 10
Applying Profiles
# Apply to single endpoint
k7-console apply-profile \
--profile enterprise_strict.yaml \
--target 192.168.1.100 \
--auth-cert /etc/k7/admin.pem \
--log-level verbose
# Apply to endpoint group
k7-console apply-profile \
--profile enterprise_strict.yaml \
--group "engineering-team" \
--auth-cert /etc/k7/admin.pem \
--schedule "2024-01-15T02:00:00Z"
# Validate profile before deployment
k7-console validate-profile --file enterprise_strict.yaml
API Integration
Python Client Example
import os
import requests
from typing import Dict, List
class K7SecurityClient:
def __init__(self, base_url: str, cert_path: str):
self.base_url = base_url.rstrip('/')
self.session = requests.Session()
self.session.cert = cert_path
self.session.verify = True
def apply_profile(self, profile_data: Dict, targets: List[str]) -> Dict:
"""Apply security profile to multiple endpoints"""
payload = {
"profile": profile_data,
"targets": targets,
"async": True
}
response = self.session.post(
f"{self.base_url}/api/v1/profiles/apply",
json=payload,
timeout=30
)
response.raise_for_status()
return response.json()
def get_endpoint_status(self, endpoint_id: str) -> Dict:
"""Retrieve current protection status for endpoint"""
response = self.session.get(
f"{self.base_url}/api/v1/endpoints/{endpoint_id}/status",
timeout=10
)
response.raise_for_status()
return response.json()
def query_threats(self, start_time: str, severity: str = "high") -> List[Dict]:
"""Query threat events from centralized log"""
params = {
"start": start_time,
"severity": severity,
"limit": 1000
}
response = self.session.get(
f"{self.base_url}/api/v1/threats/query",
params=params,
timeout=30
)
response.raise_for_status()
return response.json()["events"]
def trigger_ai_analysis(self, event_id: str) -> Dict:
"""Request AI analysis for specific threat event"""
payload = {"event_id": event_id}
response = self.session.post(
f"{self.base_url}/api/v1/ai/analyze",
json=payload,
timeout=60
)
response.raise_for_status()
return response.json()
# Usage
client = K7SecurityClient(
base_url=os.environ["K7_CONSOLE_URL"],
cert_path="/etc/k7/admin.pem"
)
# Deploy profile to engineering workstations
profile = {
"profile_name": "Engineering Developer",
"enforcement_level": "balanced",
"protection_layers": {
"executable_guard": {
"state": "enabled",
"exceptions": ["/opt/dev-tools/*"]
}
}
}
result = client.apply_profile(
profile_data=profile,
targets=["192.168.1.10", "192.168.1.11", "192.168.1.12"]
)
print(f"Deployment job ID: {result['job_id']}")
# Check endpoint status
status = client.get_endpoint_status("192.168.1.10")
print(f"Protection active: {status['protection_active']}")
print(f"Last scan: {status['last_scan_timestamp']}")
# Query recent high-severity threats
threats = client.query_threats(
start_time="2024-01-01T00:00:00Z",
severity="critical"
)
for threat in threats:
print(f"Threat: {threat['type']} on {threat['endpoint_id']}")
# Request AI analysis
analysis = client.trigger_ai_analysis(threat["event_id"])
print(f"AI recommendation: {analysis['recommendation']}")
Webhook Integration for Slack Notifications
import os
import hmac
import hashlib
from flask import Flask, request, jsonify
app = Flask(__name__)
WEBHOOK_SECRET = os.environ["K7_WEBHOOK_SECRET"]
SLACK_URL = os.environ["SLACK_WEBHOOK_URL"]
@app.route("/k7-webhook", methods=["POST"])
def handle_k7_event():
# Verify signature
signature = request.headers.get("X-K7-Signature")
body = request.get_data()
expected = hmac.new(
WEBHOOK_SECRET.encode(),
body,
hashlib.sha256
).hexdigest()
if not hmac.compare_digest(signature, expected):
return jsonify({"error": "Invalid signature"}), 403
event = request.json
# Format Slack message
slack_payload = {
"text": f"🚨 K7 Security Alert: {event['threat_type']}",
"blocks": [
{
"type": "section",
"text": {
"type": "mrkdwn",
"text": f"*Threat Detected*\n*Type:* {event['threat_type']}\n*Endpoint:* {event['endpoint_id']}\n*Severity:* {event['severity']}"
}
},
{
"type": "section",
"text": {
"type": "mrkdwn",
"text": f"*AI Analysis:*\n{event.get('ai_recommendation', 'Pending analysis...')}"
}
}
]
}
# Forward to Slack
requests.post(SLACK_URL, json=slack_payload)
return jsonify({"status": "processed"}), 200
if __name__ == "__main__":
app.run(host="0.0.0.0", port=8080)
Common Deployment Patterns
Mass Deployment via Ansible
# k7-deploy.yml
---
- name: Deploy K7 Total Security to fleet
hosts: all_endpoints
become: yes
vars:
k7_console_url: "https://console.example.com:4451"
k7_profile: "{{ lookup('file', 'profiles/{{ inventory_hostname_short }}.yaml') }}"
tasks:
- name: Install K7 agent (Debian/Ubuntu)
apt:
deb: "https://download.k7computing.com/k7-agent-linux-16.0.1195.deb"
state: present
when: ansible_os_family == "Debian"
- name: Configure agent connection
template:
src: agent-config.j2
dest: /etc/k7/agent.conf
mode: '0600'
notify: restart k7-agent
- name: Apply security profile
command: >
k7-console apply-profile
--profile {{ k7_profile }}
--target {{ ansible_default_ipv4.address }}
--auth-cert /etc/k7/admin.pem
delegate_to: localhost
handlers:
- name: restart k7-agent
service:
name: k7-agent
state: restarted
Dynamic Policy Based on Host Role
def generate_profile_for_role(role: str) -> Dict:
"""Generate K7 profile based on host role"""
base = {
"version": "16.0.1195",
"enforcement_level": "adaptive",
"logging": {"verbose": True, "retention_days": 90}
}
role_configs = {
"web_server": {
"web_filter": {"state": "disabled"},
"network_protection": {
"firewall_mode": "strict",
"allowed_ports": [80, 443]
}
},
"database_server": {
"usb_filter": {"state": "enabled", "policy": "block_all"},
"network_protection": {
"firewall_mode": "strict",
"allowed_ports": [3306, 5432]
}
},
"developer_workstation": {
"executable_guard": {
"exceptions": [
"/opt/dev-tools/*",
"/home/*/workspace/*"
]
},
"memory_scanner": {"sensitivity": "medium"}
}
}
base["protection_layers"] = role_configs.get(role, {})
base["profile_name"] = f"{role.replace('_', ' ').title()} Profile"
return base
# Usage
db_profile = generate_profile_for_role("database_server")
Troubleshooting
Agent Not Connecting to Console
# Check agent status
sudo systemctl status k7-agent
# Verify network connectivity
telnet console.example.com 4451
# Check agent logs
sudo tail -f /var/log/k7/agent.log
# Test certificate authentication
openssl s_client -connect console.example.com:4451 \
-cert /etc/k7/agent.pem -key /etc/k7/agent.key
# Reconfigure console URL
sudo k7-agent --reconfigure --console-url https://new-console.example.com:4451
Profile Application Fails
# Validate profile syntax
k7-console validate-profile --file myprofile.yaml
# Check for conflicting policies
k7-console profile-diff \
--current-endpoint 192.168.1.100 \
--new-profile myprofile.yaml
# Force reapplication
k7-console apply-profile \
--profile myprofile.yaml \
--target 192.168.1.100 \
--force \
--log-level debug
High CPU Usage from Memory Scanner
# Adjust scan frequency and exclusions
memory_scanner:
state: enabled
sensitivity: "medium" # Changed from "high"
scan_interval_seconds: 600 # Increased from 180
exclude_processes:
- "chrome"
- "firefox"
- "docker"
exclude_memory_regions:
- type: "shared_libraries"
- path: "/usr/lib/*"
AI Integration Not Responding
import os
import requests
def test_ai_endpoint():
"""Verify AI provider connectivity"""
# Test OpenAI
openai_key = os.environ.get("K7_OPENAI_API_KEY")
if openai_key:
response = requests.post(
"https://api.openai.com/v1/chat/completions",
headers={"Authorization": f"Bearer {openai_key}"},
json={
"model": "gpt-4-turbo",
"messages": [{"role": "user", "content": "test"}],
"max_tokens": 5
},
timeout=10
)
print(f"OpenAI status: {response.status_code}")
# Test Claude
claude_key = os.environ.get("K7_CLAUDE_API_KEY")
if claude_key:
response = requests.post(
"https://api.anthropic.com/v1/messages",
headers={
"x-api-key": claude_key,
"anthropic-version": "2023-06-01"
},
json={
"model": "claude-3-opus-20240229",
"messages": [{"role": "user", "content": "test"}],
"max_tokens": 5
},
timeout=10
)
print(f"Claude status: {response.status_code}")
test_ai_endpoint()
USB Filter Blocking Legitimate Devices
# List all connected USB devices with vendor/product IDs
lsusb
# Get device serial number
udevadm info --name=/dev/sdb | grep SERIAL
# Add to profile allowlist
# usb_filter:
# allow_list:
# - vendor_id: "0x0781"
# product_id: "0x5581"
# serial: "ACTUAL_SERIAL_HERE"
Best Practices
- Use environment variables for all secrets — Never hardcode API keys or credentials
- Test profiles in staging — Validate with
k7-console validate-profilebefore production deployment - Enable verbose logging initially — Reduce to
infolevel after stabilization - Implement webhook verification — Always validate
X-K7-Signatureheader - Monitor AI integration costs — Set
max_tokenslimits to control API usage - Schedule scans during off-hours — Adjust
scan_interval_secondsbased on workload patterns - Maintain exception lists centrally — Store in version control, apply via automation
- Use role-based profiles — Don't apply one-size-fits-all policies across heterogeneous environments