Build versus buy: the detection-engineering cost model
September 25, 2026 · SOCPrime · Severity: MEDIUM
This article discusses the build versus buy decision for threat detection content, recommending a hybrid approach where commodity detections are purchased and unique ones are built in-house. It emphasizes measuring both by time-to-coverage and provides a table of factors to guide the decision. 📌 **Analyst Note:** This framework helps security teams optimize detection engineering resources by focusing on what is unique to their environment while leveraging external content for common threats, reducing time-to-coverage.
Build versus buy is the decision about which threat detections a security team authors in-house and which it sources as ready-made detection content. The answer is almost never all build or all buy. Buy the commodity layer, build what is unique to your environment, and measure both sides by time-to-coverage: the elapsed time from a technique becoming public to a validated, firing detection running in your own stack.
Should my security team write our own detection rules or buy detection content?
Both. The split depends on four factors, not a preset ratio.
| Factor | Build indicator | Buy indicator |
|---|---|---|
| Telemetry uniqueness | Custom apps, proprietary protocols, environment-specific logic | Commodity TTPs against common platforms, mappable to MITRE ATT&CK techniques |
| Staffing | Detection engineers on staff with authoring and maintenance capacity | No detection-engineering FTE, or FTE consumed by other work |
| Validation capacity | Team can prove rules fire and produce audit evidence | No validation pipeline |
| Time-to-coverage target | Tolerable window measured in weeks | Window measured in hours or days |
Time-to-coverage is the factor the whole decision turns on. It measures the window between a technique becoming public and a validated, firing detection running in your environment. Every day inside that window is exposure the team chose to carry.
What does a detection engineer cost, fully loaded, and what does the backlog cost?
The answer is your own numbers. Every organization’s cost structure is different, and no external benchmark substitutes for your loaded cost, ramp timeline, or attrition rate. What matters is calculating it completely.
A fully loaded detection engineer costs more than a salary line. The total includes salary, benefits, tooling, training, and the management overhead a specialized hire carries.
Add the ramp period. A new engineer does not produce validated detections against your specific telemetry and SIEM configuration on day one, and the backlog grows while they ramp.
Add attrition. Detection engineers are scarce and mobile. Each departure restarts the ramp clock and takes environment-specific knowledge with it.
| Factor | Your figure | Notes |
|---|---|---|
| Loaded annual cost (salary + benefits + tooling + overhead) | ||
| Ramp time to productivity on your stack | Months before the hire writes production rules | |
| Annual attrition rate | Each departure restarts the ramp clock | |
| Backlog carrying cost | Risk carried while rules sit unwritten | |
| Rules authored per engineer per month | Validated, deployed rules only | |
| Platform count (SIEM, EDR, XDR targets) | Each target multiplies maintenance cost |
The backlog itself has a cost. Every rule that sits unwritten or unvalidated is a detection gap the environment runs with until someone writes, tests, and deploys it. Quantifying that carrying cost is difficult, which is why most teams skip it and why the gap persists. The worksheet at the end of this page provides the structure to calculate it.
How do small security teams keep detection rules current against new threats?
The constraint is time, not skill. Keeping hundreds or thousands of rules current while new techniques arrive continuously is a different workload from authoring them. A small team cannot do it at the speed new techniques emerge while also tuning rules, handling incidents, and maintaining the SIEM.
Three realities compound the problem:
- Technique volume exceeds authoring capacity. Threat-intelligence feeds publish new techniques continuously. A team of one or two detection engineers cannot keep pace while handling everything else the SOC demands
- Platform count multiplies build cost. Two SIEM or EDR platforms means every rule needs two implementations. Three platforms, three. Build cost scales linearly with targets.
- Validation is the bottleneck. A rule that parses is not a rule that fires. Proving it fires on real or emulated events, and that it produces audit-grade evidence, is where the time goes. Without validation capacity, bought or built content is equally unproven.
Portable detection content addresses the commodity layer directly. Rules authored in a vendor-agnostic format like Sigma and translated to each target platform give small teams continuously updated coverage without authoring and maintaining every rule from scratch.
The team’s own engineers then focus on detections that require knowledge of the environment: custom applications, internal telemetry sources, and business logic no external library can anticipate. Vendor-agnostic content narrows the window between technique publication and deployed detection for the commodity layer, freeing constrained engineering capacity for the work only that team can do.
SOC Prime’s Advanced Threat Detection library supplies curated behavior rules for emerging threats, centrally managed, deployable at scale, and stored as Detection as Code.
What factors should I consider when calculating the cost of hiring detection engineers versus buying solutions?
Seven factors, each a cell in the cost model. Fill in each from your own data. Estimates carry risk: underestimating any single factor makes the whole model unreliable.
| Factor | What to measure | Why it matters |
|---|---|---|
| Loaded cost per engineer | Salary, benefits, tooling, training, management overhead | Baseline unit economics of building in-house |
| Ramp time | Months from hire date to first validated production rule | Detection engineering is stack-specific |
| Attrition and re-hire cost | Turnover rate times ramp cost per replacement | Every departure resets the clock and reopens the backlog |
| Backlog carrying cost | Unwritten rules times assessed risk per gap | The cost paid while the queue does not move |
| Platform count | SIEM, EDR, XDR targets maintained | Each platform multiplies every build and every update |
| Evidence burden | Hours producing audit and regulatory documentation: log-source inventories, rule-deployment records, proof-of-firing evidence, dated coverage reports | Real cost whether content is built or bought |
| Time-to-coverage target | Hours or days from technique publication to validated deployed detection | Converts all the above into an operating requirement |
When the total cost of building exceeds the cost of buying the commodity layer plus the cost of building only what is unique, the decision has made itself. No universal salary figure or ratio applies. The answer depends on your numbers.
How can I justify the investment in detection content to the board or budget committee?
Frame it as what it is: converting a variable, hard-to-retain hiring problem into a predictable subscription line. The board funds coverage, speed, and risk reduction. Present detection content as the mechanism that delivers them.
Detection engineering talent is scarce, hiring cycles are long, and attrition resets the ramp. A subscription to vendor-agnostic detection content converts part of that variable cost into a fixed line item the board can plan against.
Three metrics a board recognizes:
- Time-to-coverage. The window between a technique becoming public and a validated, firing detection deployed in your environment. Buying the commodity layer compresses this window. Show the board the current window and the target.
- Validated coverage against MITRE ATT&CK. The share of relevant ATT&CK techniques for which you have a detection proven to fire. Measurable, auditable, and comparable across reporting periods. Boards approve
Key Takeaways
- Build versus buy is the decision about which threat detections a security team authors in-house and which it sources as ready-made detection content, and the answer is almost never all build or all buy.
- Buy the commodity layer, build what is unique to your environment, and measure both sides by time-to-coverage: the elapsed time from a technique becoming public to a validated, firing detection running in your own stack.
- The split depends on four factors: telemetry uniqueness, staffing, validation capacity, and time-to-coverage target, not a preset ratio.