The Fallacy of the "Blanket Ban"

For years, the standard response to regional fraud spikes was a firewall rule dropping all packets from specific ISO 3166-1 alpha-2 country codes. While effective at reducing noise, this approach creates significant collateral damage:

Expatriate Friction: Legitimate users traveling or working in high-risk regions are locked out.
B2B False Positives: Global enterprises routing traffic through regional hubs are blocked.
VPN Evasion: Sophisticated attackers simply route traffic through residential proxies in "safe" countries, rendering the geo-block useless.

Modern security posture requires Contextual Geo-Fencing—evaluating the IP address not just by location, but by connection type and reputation.

Strategy: The Nuanced Filter

Instead of a binary DROP based on location, implement a logic gate that combines location with threat intelligence data.

The Logic Flow:

Identify Location: Extract Country Code.
Assess Context: Is the IP a residential line, a cellular network, or a data center?
Check Anonymizers: Is the user masking their location via VPN, Tor, or Proxy?

If a user connects from a "High-Risk" country, block only if secondary risk factors (VPN, Hosting ASN) are present. If the connection is a clean residential ISP, trigger a step-up authentication (2FA/Captcha) rather than a hard block.

Implementation

Below is a conceptual implementation using Python to integrate this logic into a middleware layer.

Python Logic Example

import requests

IPASIS_API_KEY = "your_api_key"
HIGH_RISK_COUNTRIES = {"XX", "YY", "ZZ"} # ISO codes

def analyze_request(client_ip):
    # Query IPASIS Intelligence
    url = f"https://api.ipasis.com/json/{client_ip}?key={IPASIS_API_KEY}"
    response = requests.get(url, timeout=2)
    
    if response.status_code != 200:
        return "FAIL_OPEN" # Avoid blocking if API is unreachable
        
    data = response.json()
    country = data.get('country_code')
    security = data.get('security', {})
    
    # 1. Global Threat Block (Regardless of Country)
    if security.get('is_bogon') or security.get('is_tor'):
        return "BLOCK_CRITICAL"

    # 2. Contextual Geo-Blocking
    if country in HIGH_RISK_COUNTRIES:
        # Block if they are hiding behind a VPN/Proxy within a high-risk zone
        if security.get('is_vpn') or security.get('is_proxy'):
            return "BLOCK_HIGH_RISK"
            
        # Block Data Center traffic (often bots)
        if data.get('asn_type') == 'hosting':
            return "BLOCK_BOT"
            
        # Allow Residential/Mobile but Flag for Review/Captcha
        return "CHALLENGE"

    # 3. "Safe" Country Check
    # Still check for high-risk proxies tunneling into safe zones
    if security.get('is_proxy') and security.get('threat_score') > 80:
        return "BLOCK_ANOMALY"

    return "ALLOW"

Handling The "Safe Country" Proxy Problem

A common attack vector involves fraud farms in high-risk nations routing traffic through residential proxies located in low-risk nations (e.g., the US or UK).

Simple geo-blocking fails here because the IP looks domestic. To counter this, your logic must prioritize is_proxy and is_residential_proxy flags over the country_code. If a request originates from the US but triggers a high_risk_proxy flag, it should be treated with the same scrutiny as traffic from a sanctioned region.

Architecture Consideration: Edge vs. App Level

WAF/Edge (Cloudflare/AWS WAF): Good for volumetric DDoS and static country blocks. Less capable of granular JSON parsing and logic branching based on API responses.
Application Middleware: Ideal for the logic described above. It adds milliseconds of latency but prevents 99% of fraud while saving conversion rates.

FAQ

Q: How does this impact API latency?
A: IPASIS response times are typically sub-50ms. By caching results for repeat IPs (e.g., in Redis) with a TTL of 10-15 minutes, the performance impact is negligible.

Q: What about legitimate users using VPNs?
A: Not all VPNs are equal. Distinguish between corporate VPNs and anonymous VPNs. If a user is logging in (authenticated session) via a known VPN, allow it. If it is a registration attempt (anonymous) via a VPN, block or challenge it.

Q: Can I automate the "High Risk" country list?
A: Yes. Rather than hardcoding ISO codes, you can aggregate data from your chargeback reports. If >2% of transactions from Country X result in fraud, programmatically add Country X to the high-risk array.

Secure Your Perimeter with Data, Not Guesses

Blanket bans are a blunt instrument in a precision game. To maximize conversion while minimizing risk, you need granular visibility into every connection.

Get your free API Key from IPASIS today and start filtering traffic based on intelligence, not just geography.

Geo-Blocking Strategies: Mitigating High-Risk Country Traffic Without Impacting Conversion