Threat hunting is an essential practice in modern cybersecurity. We asked industry experts to share an example of how they’ve used threat hunting to proactively identify and mitigate threats. Here are the methodologies and tools they’ve found to be effective. Gain valuable insights into proactive threat detection strategies.
- Uncover Threats with Impossible Travel Analysis
- Combine Tools for Proactive Threat Detection
- Leverage MITRE ATT&CK in Threat Hunting
- Employ Hypothesis-Driven Threat Hunting Approach
4 Threat Hunting Success Stories
Uncover Threats with Impossible Travel Analysis
The most revealing threat hunting exercise I’ve implemented is regular “impossible travel” analysis within authentication logs. During one investigation for a client, this approach uncovered a sophisticated compromise that had evaded automated detection systems for weeks.
While reviewing authentication patterns, we identified login sequences from London and San Francisco occurring within unrealistic timeframes—technically possible but practically suspicious. Traditional security tools hadn’t flagged these events because each individual login used valid credentials and passed MFA challenges, appearing legitimate when viewed in isolation.
Digging deeper, we discovered a threat actor had compromised a developer’s OAuth tokens rather than their actual credentials. This allowed them to maintain persistence while bypassing standard authentication alerts. The compromise was leveraging connection pooling to disguise their true origin, but the time-based analysis revealed the underlying pattern.
What makes this methodology particularly effective is its focus on behavioral anomalies rather than known signatures. By prioritizing impossible travel, unusual access times, and atypical resource requests, we identify sophisticated threats that signature-based detection systematically misses.
Our approach combines log aggregation across disparate systems (authentication, VPN, cloud services) with temporal analysis scripts that flag potentially impossible activity patterns. The key insight was creating baselines for normal user behavior before hunting for deviations, which dramatically reduced false positives.
The remediation process was equally instructional—rather than simply resetting credentials, we implemented OAuth token rotation, refined MFA implementation to include location awareness, and enhanced session monitoring. This holistic response addressed not just the immediate compromise but the systemic weaknesses that enabled it.
For organizations looking to enhance their threat hunting capabilities, I recommend starting with temporal analysis of authentication events before investing in more complex technologies. The signal-to-noise ratio is remarkably favorable, and the technical barriers to implementation are minimal compared to more sophisticated hunting techniques.
Simon Lewis
Co-Founder, Certo Software
Combine Tools for Proactive Threat Detection
Proactive threat hunting is a central part of how we keep business networks secure—especially for small- and medium-sized companies that might not have internal SOCs. One concrete example: after noticing unusual outbound traffic from a financial sector client, we leveraged tools like SentinelOne EDR and our SIEM platform to correlate logs across user endpoints and cloud accounts. By combining automated analysis with manual investigation, we were able to pinpoint a previously unknown phishing campaign that was bypassing existing filters.
We lean heavily on the MITRE ATT&CK framework to structure our hunting—mapping suspicious activity from log sources (firewall, AD, endpoint) against known TTPs. For instance, when we see privilege escalation attempts, we drill down with threat emulation tools to test the resiliency of our clients’ defenses.
A methodology that proves effective is layering real-time network traffic analysis (using tools like Darktrace or Elastic Security) with regular user behavior analytics audits. These techniques can reveal insider threats or compromised credentials early, giving us a head start in containing the risk before it matures into a breach.
When we identify a threat, our playbook includes isolating the affected host(s), running forensic snapshots, and—if needed—rolling out known-good backups to eliminate persistence. The key is never waiting for an alert; we actively look for what shouldn’t be happening on the network, and that’s how we stay a step ahead.
Steve Payerle
President, Next Level Technologies
Leverage MITRE ATT&CK in Threat Hunting
One instance where we used threat hunting proactively was during a platform stress test we conducted internally. Rather than waiting for alerts, our team took a hypothesis-driven approach, assuming an insider threat scenario where access privileges might be misused in low-visibility windows.
We combined behavioral analytics through Elastic Stack with Zeek network monitoring to identify unusual lateral movement patterns. These patterns weren’t triggering any automated alerts, but through manual log correlation and threat intelligence feeds, we uncovered a set of compromised credentials from a test environment that had been reused elsewhere.
By catching this early, we locked down access pathways and helped inform a client-facing guide on secure access protocols. Tools like Osquery and custom YARA rules have proven especially effective in these deep-dive investigations. It’s this proactive, hands-on approach that we embed into both our internal practices and the talent strategies we build for clients.
Amit Doshi
Founder & CEO, MyTurn
Employ Hypothesis-Driven Threat Hunting Approach
Security has always been a top priority for us at SmythOS, especially as we scale integrations across a growing ecosystem. Early on, we realized that waiting for alerts wasn’t enough—we needed a more proactive approach to threat detection.
That’s when we started building AI-driven threat hunting into our architecture. We deployed autonomous agents to monitor API interactions across our platform, specifically looking for anomalies like irregular data access patterns or deviations in usage behavior.
At one point, these agents flagged an unexpected spike in data queries from a third-party integration. It turned out to be a vulnerability in that vendor’s API, something that hadn’t yet been disclosed publicly. Because of the early detection, we were able to mitigate it before it became a risk for our users.
We used tools like Agent Weaver to build and manage the monitoring agents. Additionally, we integrated analytics from platforms like OpenAI and Hugging Face to improve detection accuracy through behavioral modeling.
Our methodology emphasizes real-time monitoring, anomaly detection, and continuous learning so the system gets smarter over time.
My advice to other teams: don’t just rely on perimeter defenses. Invest in automation that can hunt threats continuously, and train your systems to learn from what they find. The threats are evolving rapidly, but with the right tools, your response can evolve faster.
Alexander De Ridder
Co-Founder & CTO, SmythOS.com
