Background

Challenge

The environment presented three compounding issues:

• Legacy middleware that normalized and forwarded transaction metadata before sanctions screening, creating a race where attacker-crafted metadata could slip through.
• Weak telemetry for outbound connections from settlement systems — insufficient network/host signatures to detect misuse or command-and-control.
• Regulatory risk: potential fines for processing payments tied to sanctioned entities and reputational damage.

Solution

The program aligned three streams: sanctions data integrity, hardened infrastructure, and enhanced detection. Key components:

1. Canonical, machine-readable sanctions screening upstream — all transaction metadata passed through an immutable screening service before routing to processors.
2. Network and host hardening (segmentation, eBPF-based flow logging, and allowlists) to reduce lateral movement and unapproved exfil.
3. Detection engineering using signature and behavior rules (Suricata/Zeek, YARA, Sigma) mapped to threats observed in public and underground actor discussions.

Implementation (technical detail)

1. Sanctions screening microservice

• A canonical screening service was placed at the strict inbound edge: every transaction payload was validated, normalized, and scanned against the latest sanctions lists (OFAC, EU, UN) prior to any routing.

Defensive example: fetch OFAC SDN and cache (simplified)
import requests, json, time
def fetchofacjson():
r = requests.get(URL + "/download-json")
r.raiseforstatus()
return r.json()

Legal Protection Matters: Cybersecurity incidents often have significant legal implications. Our sister firm Steele Family Law helps Illinois families navigate complex legal situations with the same commitment to protection and discretion we bring to cybersecurity.


integrate into screening DB (pseudo)
screeningdb.upsert(fetchofacjson())

    

Example Suricata/IDS signature (defensive detection)
alert http any any -> any any (msg:"ALERT Suspicious HTTP UA matching known C2 pattern"; http.useragent; content:"curl/7."; sid:1000001; rev:1;)
    

3. Detection engineering & petting zoo validation

• Created Sigma rules, YARA and Splunk queries for the SOC to escalate suspected attempts to obfuscate sanctioned names (character substitutions, homoglyphs) or to use sub-domains for sanctions bypass.

rule SuspiciousJNDIPatterns_Defensive {
meta:
description = "Detects jndi-like patterns in logs (defensive only)"
strings:
$a = /\$\s\{\sjndi\s*:/i
condition:
$a
}
    

Results

Within six months Atlas achieved:

• Zero incidents of processing confirmed sanctioned transactions after deployment (audit evidence: transaction logs, immutable screening outputs).
• 50% reduction in false-positive manual reviews through canonical normalization and fuzzy-matching algorithms tuned for homoglyph/alias detection.
• Reduced mean time to detect (MTTD) for suspicious outbound telemetry from 28 hours to 75 minutes thanks to eBPF flow logs and Suricata rules.
• Successful regulatory audit with no material findings related to sanctions processing.

Lessons Learned

• Push sanctions screening as close to the edge as possible and make the screening service immutable and auditable.

• Detection must include both signature-based and behavior-based rules. Attackers will attempt evasion (e.g., homoglyphs, char substitutions, metadata padding) — tune fuzzy-match thresholds and log canonicalization rules.

• Use safe, community POCs for validation. Avoid running live exploit code against production systems; instead use frameworks that emulate TTPs for detection testing.

External validation & resources

Responsible disclosure and validation tools used:

• OWASP WebGoat (secure lab to test app-layer detection): owasp.org/www-project-webgoat
• Vulnerability databases for referenced CVEs and vendor mitigations: NVD (nvd.nist.gov) and MITRE CVE (cve.mitre.org)
• Sanitized analysis of underground forum chatter and exploitation trends: Recorded Future and KrebsOnSecurity provide public reporting and timelines (see Recorded Future and KrebsOnSecurity).

On bug bounties and researcher engagement: Atlas maintained a public-facing vulnerability disclosure program and partnered with platforms such as HackerOne and Bugcrowd. Public reports from these platforms document bounty ranges and community behavior — for program design reference see HackerOne resources (public program reports) and average payout trendlines on their site: HackerOne resources.

Appendix — Threat & evasions (high level)

• Credential stuffing and account takeover to route payments via mule accounts — detect by anomalous IP->account mapping, velocity rules, and device fingerprint changes.
• Metadata obfuscation to evade sanctions names (homoglyphs, Unicode mixing) — use canonical normalization and fuzzy-matching algorithms; alert on improbable unicode mixes.
• Using cloud functions or third-party APIs as relay points — monitor for unusual TLS endpoints, short-lived certificates, and atypical egress patterns.