Why do proprietary SIEM languages often slow detection engineering and increase operating cost?

Why proprietary SIEM query languages create engineering drag

detection engineering depends on being able to express a security idea once, then adapt it across telemetry types, data models, and environments. Proprietary SIEM languages often break that pattern. They can make simple logic look different in every product, reduce portability between teams, and force engineers to learn platform-specific syntax before they can validate whether a detection idea is actually good.

The main cost is not just syntax friction. When logic cannot be modularised cleanly, teams spend more time translating detections than improving them. That affects rule review, peer reuse, and coverage expansion, especially when a team runs multiple log sources or multiple security tools. In practice, the language becomes part of the operating model, not just the query interface.

• Custom syntax increases translation overhead when analysts move from one source type to another.
• Non-modular logic makes it harder to reuse tested detection patterns across use cases.
• Platform-specific constructs can slow onboarding because engineers must learn product behaviour, not just detection intent.

That is why more reusable detection stacks tend to favour shared abstractions, consistent field models, and logic that survives implementation changes. If the detection logic is trapped inside one vendor grammar, the organisation pays repeatedly for the same idea.

For a broader practitioner view of how detection operations benefit from reusable controls and standardised defensive workflows, the MITRE D3FEND knowledge graph is useful because it links defensive techniques to repeatable countermeasure concepts rather than product-specific syntax.

Why operating cost rises as the language gets more proprietary

Operating cost rises because the language affects more than authoring time. It influences maintenance, QA, tuning, migration, and staff productivity. A team with portable detection logic can carry patterns forward as log sources change. A team locked into a proprietary grammar often has to re-implement those patterns, re-test them, and re-document them whenever the data shape or platform changes.

The hidden expense is services and expertise concentration. Organisations often need specialist consulting or scarce in-house experts to maintain the language, especially when detections become complex. That creates a higher cost base and a stronger dependency on a small number of people who understand both the security problem and the vendor syntax.

• Rewrite cost grows when each data source needs a separate expression of the same detection.
• Tuning cost grows when one product’s language cannot be linted, reviewed, or tested with shared tooling easily.
• Migration cost grows when platform change requires a near full rewrite rather than a transformation.

Teams also lose compounding efficiency. In a more standardised environment, one detection improvement can benefit many rules. In a proprietary environment, each improvement is more local, so the organisation keeps paying for repeated maintenance instead of building a detection library.

For practitioners who want a catalogue of defensive patterns and detection-adjacent security guidance, SANS Security Resources is a practical reference point because it reflects the operational reality of SOC and detection work rather than only the theory.

What good detection engineering looks like in practice

The goal is not “no vendor specificity”, because every platform has some implementation detail. The goal is to keep the security logic as stable and portable as possible, while limiting the part that is tied to a specific SIEM. Good detection engineering separates the analytic intent from the platform wrapper, uses shared naming and field conventions, and keeps tests, versioning, and review tight enough that changes are measurable.

That approach improves both speed and cost control. It shortens time to production for new detections, reduces the risk of duplicate logic drifting apart, and makes it easier to scale the same pattern across sources. It also makes staffing more resilient, because newer engineers can learn the detection model faster when they are not forced to master a bespoke language first.

Practitioner Guidance: Treat query portability as an operating cost control, not a cosmetic engineering preference. If a detection cannot be reused, tested, or migrated with modest effort, it is effectively a bespoke asset with a higher lifetime maintenance bill.

Practitioner Guidance: Prioritise standardised field mapping, modular detection logic, and clear test cases before adding more detections. The common failure mode is buying coverage growth at the expense of maintainability, then discovering that most of the budget is being spent on translation and tuning rather than coverage.

Practitioner takeaway: Proprietary SIEM languages slow detection engineering because they force the organisation to pay repeatedly for translation, maintenance, and expertise, while portable logic lets one improvement scale across many detections.

Related resources from NHI Mgmt Group

• What breaks when detection engineering depends on batch processing and proprietary SIEM logic?
• How do security teams decide whether SIEM cost optimisation is hurting detection?
• Why do AI cost overruns often indicate an identity or agent operating outside its intended scope?
• Who should own detection engineering when the security stack already has a SIEM and SOC?