| name | k7-total-security-deployment-framework |
| description | Deploy and configure K7 Total Security 16.0.1195 multi-platform endpoint protection with AI-powered threat analysis |
| triggers | ["configure K7 Total Security deployment","set up K7 endpoint protection policies","create K7 security profile for enterprise","integrate K7 with OpenAI threat analysis","deploy K7 Total Security across multiple platforms","troubleshoot K7 security agent configuration","automate K7 policy distribution","configure K7 USB filtering and web protection"] |
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
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
sudo ./install.sh --silent --accept-license
k7-console --version
Agent Deployment
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"
curl -O https://download.k7computing.com/k7-agent-mac-16.0.1195.pkg
sudo installer -pkg k7-agent-mac-16.0.1195.pkg -target /
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.
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
k7-console apply-profile \
--profile enterprise_strict.yaml \
--target 192.168.1.100 \
--auth-cert /etc/k7/admin.pem \
--log-level verbose
k7-console apply-profile \
--profile enterprise_strict.yaml \
--group "engineering-team" \
--auth-cert /etc/k7/admin.pem \
--schedule "2024-01-15T02:00:00Z"
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()
client = K7SecurityClient(
base_url=os.environ["K7_CONSOLE_URL"],
cert_path="/etc/k7/admin.pem"
)
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']}")
status = client.get_endpoint_status("192.168.1.10")
print(f"Protection active: {status['protection_active']}")
print(f"Last scan: {status['last_scan_timestamp']}")
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']}")
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():
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
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...')}"
}
}
]
}
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
---
- 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
db_profile = generate_profile_for_role("database_server")
Troubleshooting
Agent Not Connecting to Console
sudo systemctl status k7-agent
telnet console.example.com 4451
sudo tail -f /var/log/k7/agent.log
openssl s_client -connect console.example.com:4451 \
-cert /etc/k7/agent.pem -key /etc/k7/agent.key
sudo k7-agent --reconfigure --console-url https://new-console.example.com:4451
Profile Application Fails
k7-console validate-profile --file myprofile.yaml
k7-console profile-diff \
--current-endpoint 192.168.1.100 \
--new-profile myprofile.yaml
k7-console apply-profile \
--profile myprofile.yaml \
--target 192.168.1.100 \
--force \
--log-level debug
High CPU Usage from Memory Scanner
memory_scanner:
state: enabled
sensitivity: "medium"
scan_interval_seconds: 600
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"""
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}")
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
lsusb
udevadm info --name=/dev/sdb | grep SERIAL
Best Practices
- Use environment variables for all secrets — Never hardcode API keys or credentials
- Test profiles in staging — Validate with
k7-console validate-profile before production deployment
- Enable verbose logging initially — Reduce to
info level after stabilization
- Implement webhook verification — Always validate
X-K7-Signature header
- Monitor AI integration costs — Set
max_tokens limits to control API usage
- Schedule scans during off-hours — Adjust
scan_interval_seconds based 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