Detection-as-code is closing the security operations gap

At a glance

What this is: This is an analysis of how detection-as-code applies software engineering discipline to security operations, with the key finding that manual SOC workflows remain the dominant operating model.

Why it matters: It matters to IAM and security practitioners because operational controls are only as strong as the processes that maintain them, and manual security workflows create avoidable governance, audit, and reliability gaps.

By the numbers:

• 69% of SOCs still rely on manual or mostly manual processes for core functions.
• 76% of organizations cite security skill gaps as a barrier to security-as-code adoption.
• 85% of organizations lack full visibility into third-party vendors connected via OAuth apps.

👉 Read Panther's analysis of detection-as-code for security operations

Context

Security operations has lagged behind infrastructure, pipelines, and policy in adopting code-based control models. In practice, that leaves detection logic edited in production interfaces, changed under pressure, and left without the audit trail, testing, or rollback discipline that most engineering teams now treat as standard. For IAM and security governance teams, the deeper issue is not tooling preference but operational control maturity.

Detection-as-code tries to close that gap by making threat detections versioned, testable, and deployable through CI/CD. That matters wherever security controls must be reviewed, approved, and reproduced, including identity-adjacent operations such as alerting on credential abuse or access anomalies. The governance lesson is straightforward: when the change process is weak, the control itself becomes hard to trust.

Key questions

Q: How should security teams implement detection-as-code in a SOC environment?

A: Start by moving high-value detection logic into Git, then add peer review, test cases, and a promotion pipeline before allowing production deployment. Focus first on rules that are noisy, business-critical, or frequently changed. The goal is not automation for its own sake. It is controlled change, reproducibility, and rollback capability for operational security logic.

Q: What breaks when detection rules are edited directly in SIEM consoles?

A: Direct SIEM edits remove version history, weaken review discipline, and make rollback difficult when a rule misbehaves. They also increase the chance of undocumented changes during incidents, which creates audit gaps and inconsistent control quality. Over time, teams lose confidence in the detections themselves because the change process is no longer observable.

Q: How do security teams know detection-as-code is actually working?

A: Look for fewer untracked production edits, faster safe deployment of rule changes, and test results that show known threats trigger as expected before rollout. Audit prep should also become easier because every rule change has a reviewer, a reason, and a history. If those signals are missing, the process is still partly manual.

Q: What is the difference between manual detection management and detection-as-code?

A: Manual detection management treats rules like mutable operational content, usually edited in a console with limited traceability. Detection-as-code treats rules like software, with Git history, automated validation, and controlled deployment. The distinction matters because the second model gives teams repeatability and governance evidence, while the first often depends on memory and informal process.

Technical breakdown

Why manual SIEM rule handling breaks down at scale

When detections are edited directly in a SIEM GUI, each change becomes effectively stateful and opaque. Teams lose version history, cannot reliably compare revisions, and often have no pre-production validation path. That creates brittle logic, inconsistent rule quality, and a heavy dependence on tribal knowledge. Detection engineering becomes harder to govern because the control plane for the control itself is undocumented. In software terms, the problem is not automation scarcity alone. It is the absence of repeatable change management for logic that can materially affect incident response and compliance evidence.

Practical implication: move detection content into version control before rule quality and auditability degrade further.

How Git, testing, and CI/CD change detection governance

Detection-as-code applies the same discipline used in software delivery. Git provides history and review. Automated tests validate that a rule behaves as intended against known inputs. CI/CD promotes only approved changes and reduces the risk of accidental regressions. This matters because detection logic is not static configuration. It is operational policy expressed in code, and policy that cannot be tested tends to drift. The key benefit is not speed alone. It is controlled change, where every modification is explainable, reproducible, and reversible.

Practical implication: require peer review and test coverage before any detection rule reaches production.

Why security operations is the last major as-code holdout

The article makes clear that SOC workflows have structural barriers that other engineering domains have already solved. Manual processes persist because security teams often inherit GUI-first tools, mixed skill sets, and integrations that are harder than they should be. The result is a gap between what teams want from modern governance and what their operational model can support. For identity and security programmes, that gap matters because controls around credentials, privileges, and alerting depend on fast, reliable operational feedback. If change control remains manual, response quality will remain uneven.

Practical implication: treat SOC workflow modernization as a governance issue, not just a tooling upgrade.

Threat narrative

Attacker objective: The objective is to impair or bypass detection quality so that threats are harder to see, slower to investigate, and easier to miss.

1. Entry occurs when detection logic is changed directly in production systems without a tested, reviewable workflow, creating opportunities for mistakes or malicious edits.
2. Escalation follows when brittle rules or undocumented changes reduce visibility, delay validation, and weaken confidence in the control environment.
3. Impact is slower detection, noisier alert queues, weaker audit evidence, and higher operational risk during incidents.

NHI Mgmt Group analysis

Detection-as-code is really a governance model for security operations, not just a tooling pattern. The core change is that detection logic becomes reviewable, testable, and reversible in the same way infrastructure code already is. That shifts SOC operations away from click-driven edits toward controlled change management, which is the only durable way to preserve trust in a control that changes every day. For practitioners, the lesson is to govern detections as production policy, not as ad hoc content.

Control drift is the named risk hiding inside manual detection workflows. When rules are edited in production interfaces, every update can introduce undocumented behaviour, inconsistent approvals, and hidden regressions. That makes audit evidence weaker and operational assurance harder to sustain. In identity-heavy environments, the same drift problem shows up when alerting on authentication anomalies or privileged access abuse cannot be reproduced or tested. Practitioners should treat drift as a control failure, not a nuisance.

Security operations is the last major function where software discipline has not yet become default practice. That lag is now a governance liability because SOCs are expected to support faster threat response, stronger compliance evidence, and more resilient operations with the same or fewer people. Code-based workflows are how teams create repeatability under pressure. The practical conclusion is that manual rule management no longer scales with the assurance demands placed on modern security programmes.

For IAM and PAM teams, the value of detection-as-code is the auditability of identity signals. Credential abuse, privilege escalation, and anomalous access events are only useful if the detections behind them can be trusted, tested, and explained. Version control and test coverage create that trust boundary. Practitioners should align detection engineering with the same governance expectations applied to access policy and privileged workflows.

This article also signals that security data platforms are moving closer to engineering platforms. Once detections are expressed as code, the practical boundary between analytics, operations, and DevSecOps becomes thinner. That favors teams that can manage code, reviews, and automation together. Practitioners should expect detection governance to become a more explicit part of broader cyber risk management.

What this signals

Detection governance is converging with identity governance. As more alerts hinge on authentication, token use, and privileged access behaviour, SOC workflows need the same traceability expected of IAM changes. That means code review, test coverage, and rollback discipline are no longer just engineering niceties. They are part of the control environment that determines whether identity signals can be trusted in practice.

Control drift will become a more visible audit issue as teams modernise SOC operations. Once detections are managed as code, gaps in review discipline and undocumented changes stand out more clearly, which is exactly why the model is useful. The immediate programme implication is that security teams will need to align detection engineering, access governance, and change management more tightly than they have in the past.

Platform convergence is pushing security teams toward engineering-grade workflows. When analytics, response, and rule delivery sit in the same operational chain, manual handling becomes the weakest link. Practitioners should expect more pressure to prove that security changes are reproducible, reviewable, and connected to broader governance controls such as NIST Cybersecurity Framework 2.0 and change accountability.

For practitioners

• Move detection rules into version control Store every high-value detection rule in Git so changes, reviewers, and rationale are preserved across incidents and audits. Start with the noisiest or most business-critical rules, then expand into the rest of the SOC content library. Use the same repository discipline you already apply to infrastructure code.
• Add automated tests for core detections Create test cases that validate expected matches, known false positives, and basic syntax before a rule is promoted. Include identity abuse scenarios such as suspicious authentication patterns, token misuse, and privileged access anomalies where relevant to your environment.
• Enforce peer review before production changes Require pull request approval for detection logic, with explicit review of the detection intent, expected data sources, and rollback path. This reduces the risk of untracked changes and creates a defensible audit trail when incident pressure rises.
• Build rollback and change traceability into SOC workflows Make it possible to revert a broken detection quickly without losing context about why the change occurred. Pair rollback capability with comments, tickets, or change references so the operational record remains usable during audits and post-incident reviews.

Key takeaways

• Manual SOC workflows remain the control gap that detection-as-code is designed to close.
• The operational value is not only speed, but version history, testability, and rollback for detection logic.
• For security and identity teams, detection governance now needs the same discipline as code-based infrastructure and access policy.

Key terms

• Detection as code: A method of managing detection logic like software, using version control, testing, and deployment pipelines. It improves change control and rollback discipline, which is especially useful when AI helps generate or tune rules that will be deployed into production.
• Control Drift: Control drift is the gradual weakening or inconsistency of a control over time as systems, workflows, or business rules change. It often appears as different interpretations, missed exceptions, or uneven enforcement across applications, and it usually becomes visible only when monitoring spans the full process.
• Security Data Lake: A security data lake is a centralised repository for storing large volumes of security telemetry in a queryable form. Unlike a narrow SIEM pipeline, it is designed to keep heterogeneous logs accessible at scale so analysts and automation can correlate identity, endpoint, cloud, network, and application evidence.
• CI/CD for detections: CI/CD for detections is the use of automated build, test, and deployment pipelines to validate security rules before they reach production. It reduces regressions, documents change history, and creates a safer path for updating detection logic under operational pressure.

What's in the full article

Panther's full article covers the operational detail this post intentionally leaves for the source:

• Example detection-as-code workflow patterns for SOC teams moving from GUI edits to Git-based change control
• Implementation detail on Python, SQL, and YAML rule authoring in a security data lake environment
• How peer review, testing, and CI/CD are wired into detection rule promotion and rollback
• Customer-specific examples showing how teams operationalised code-based detection management

👉 The full Panther article covers the SOC workflow changes, control model, and implementation examples

Deepen your knowledge

NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, machine identity security, and secrets management. It is a practical fit for practitioners who need to connect identity controls to broader security operations.