The Evidence Is Clear
CardinalOps 2025 research shows that the average SIEM covers only 21% of MITRE ATT&CK techniques despite having the data to support far broader coverage. 13% of existing SIEM rules are broken, stale, or never firing. And writing a single detection rule takes approximately five days on average.
If your detection program has 200 rules in it, statistically, 26 of them are not working. And the techniques those broken rules were supposed to cover are currently undetected in your environment.
Why Scaling Detection Is So Hard
Most security teams face three structurally impossible choices. They can hire more detection engineers, but at $150K or more per head and a global shortage of 1.4 million cybersecurity professionals, this does not scale. They can rely on vendor-provided sigma rules, which are generic, noisy, and cover only a fraction of real-world attack techniques. Or they can build internal scripts that depend entirely on individual engineers who will eventually leave.
None of these options solve the underlying problem: detection engineering is a high-skill, time-intensive discipline that cannot be done manually at the speed or scale that modern threats require.
What Automated Detection Engineering Looks Like
DefenderLens was built to solve exactly this problem. The platform accepts any threat source, from CTI reports and vendor advisories to news articles and RSS feeds, and uses AI to generate production-ready detection rules for CrowdStrike Falcon or Splunk within minutes.
Each rule is automatically mapped to MITRE ATT&CK with severity scoring and unit tests. From there, the platform handles peer review, schema validation, staging, and one-click production deployment. Full version control and rollback are standard features.
Turning Intelligence Into Coverage Fast
One of the core promises of detection engineering is that your team can operationalize threat intelligence before attackers exploit the gaps it describes. Without automation, that promise is rarely kept. With DefenderLens, intelligence becomes a deployed detection rule in minutes rather than days.
This speed compounds over time. Teams that operationalize every new advisory immediately build coverage across the MITRE ATT&CK framework ten times faster than those working through manual pipelines.
What Changes for Your Team
For enterprise SOC detection engineers:
- Reclaim 60% of time currently spent on maintenance
- Build new MITRE ATT&CK coverage daily instead of weekly
- Deploy with confidence through automated testing and review
For MSSPs and MDRs:
- Manage detection across all client tenants from one platform
- Deploy consistent rules without per-client engineering rework
- Scale coverage without scaling headcount
Integrations Already Live
DefenderLens currently integrates natively with CrowdStrike Falcon and Splunk via direct API, with Microsoft Sentinel, Elastic, and Palo Alto coming soon. Rules deploy in each platform's native syntax. No middleware. No rip-and-replace.
Conclusion
Detection engineering is not failing because of bad engineers. It is failing because the processes and tools built around it have not kept pace with the threat landscape. DefenderLens provides the automation layer that brings detection engineering into the modern era, making it faster, more accurate, and genuinely scalable.