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    <a href="/#oversight">Oversight</a>
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<header class="hero detail-hero"><div class="wrap">
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  <a class="back" href="/#labs">&larr; All work</a>
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  <div class="kicker">DETECTION ENGINEERING</div>
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  <h1>Detection-as-Code</h1>
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  <p class="tagline">Sigma rules mapped to MITRE ATT&amp;CK, linted and tested in CI, and compiled to Splunk, Elastic, and Microsoft Sentinel KQL: one rule, every SIEM.</p>
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  <div class="tags"><span>Sigma</span><span>Splunk SPL</span><span>Sentinel KQL</span><span>Elastic ES|QL</span><span>MITRE ATT&amp;CK</span><span>Sysmon</span><span>GitHub Actions</span><span>pytest</span></div>
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  <div class="facts"><div class="stat"><div class="n">10</div><div class="k">Sigma rules</div></div><div class="stat"><div class="n">3</div><div class="k">SIEM targets</div></div><div class="stat"><div class="n">9</div><div class="k">ATT&amp;CK techniques</div></div><div class="stat"><div class="n">5</div><div class="k">ATT&amp;CK tactics</div></div><div class="stat"><div class="n">27</div><div class="k">compiled queries</div></div></div>
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</div></header>
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<section><div class="wrap">
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  <figure class="shot"><img loading="lazy" src="/assets/detection-as-code/01-multi-siem-conversion.png" alt="One Sigma rule compiled to Splunk SPL, Elastic ES|QL, and Microsoft Sentinel KQL side by side."></figure><figcaption>One Sigma rule compiled to Splunk SPL, Elastic ES|QL, and Microsoft Sentinel KQL side by side.</figcaption>
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  <div class="content">
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  <h2>Repository layout</h2>
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<p>Rules live as Sigma YAML under <code>rules/</code>, split by platform and ATT&CK tactic. One compiler walks the tree and emits a query per backend into <code>dist/{splunk,esql,kusto}/</code>, mirroring the source layout. Tests, the CI workflow, and a Makefile of one-liners sit alongside.</p><pre><code>rules/
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  windows/   credential-access, execution, persistence, defense-evasion, initial-access
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  linux/     credential-access, execution, persistence
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tools/convert.py        compile every rule to Splunk / Elastic / Sentinel
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tests/test_rules.py     schema, ATT&CK tagging, unique IDs, correlation references
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.github/workflows/      lint -> test -> convert on every push
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dist/                   generated queries (CI artifact; gitignored)</code></pre><p>The pinned toolchain is small and exact: <code>sigma-cli==3.0.2</code> with the Splunk, Elasticsearch, and Kusto backends, plus the Sysmon and Windows processing pipelines. Both the linter and the converter run from that single requirements set.</p><pre><code>sigma-cli==3.0.2
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pysigma-backend-splunk==2.1.0
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pysigma-backend-elasticsearch==2.0.3
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pysigma-backend-kusto==1.0.1
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pysigma-pipeline-sysmon==2.0.0
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pysigma-pipeline-windows==2.0.0
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pytest==8.3.3
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PyYAML==6.0.3</code></pre>
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<h2>A rule in full</h2>
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<p>Rules are not naive. The LSASS detection filters on the granted-access masks that Mimikatz, comsvcs MiniDump, and similar tooling actually request rather than the broad <code>0x1010</code> alone, and excludes known legitimate readers. Source, verbatim:</p><pre><code>title: Suspicious LSASS Process Access
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id: dcfda42d-c1a7-4106-aa96-7912201d9221
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status: experimental
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description: &gt;
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  Detects process access to lsass.exe with access rights commonly used to read
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  process memory (credential dumping). Tuned to the granted-access masks seen with
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  Mimikatz, comsvcs MiniDump, and similar tooling rather than the broad 0x1010 alone.
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references:
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  - https://attack.mitre.org/techniques/T1003/001/
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  - https://github.com/SwiftOnSecurity/sysmon-config
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author: Zion Boggan
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date: 2026-05-12
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tags:
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  - attack.credential_access
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  - attack.t1003.001
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logsource:
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  product: windows
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  category: process_access
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detection:
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  selection:
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    TargetImage|endswith: '\lsass.exe'
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    GrantedAccess:
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      - '0x1010'
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      - '0x1410'
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      - '0x143a'
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      - '0x1438'
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      - '0x1fffff'
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  filter_known:
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    SourceImage|endswith:
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      - '\wininit.exe'
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      - '\csrss.exe'
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      - '\MsMpEng.exe'
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      - '\wmiprvse.exe'
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  condition: selection and not filter_known
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falsepositives:
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  - EDR and AV products legitimately reading LSASS; baseline and add to filter_known.
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level: high</code></pre>
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<h2>The compiler and pipeline selection</h2>
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<p><code>tools/convert.py</code> walks every <code>.yml</code> under <code>rules/</code> and shells out to <code>sigma convert</code> once per backend. The right processing pipeline is chosen from each rule's <code>logsource</code>: Sysmon for process, file, image-load, and network categories; Windows-audit for the Security and System channels (service installs, scheduled tasks). Rules with no matching pipeline are converted with <code>--without-pipeline</code> so generic logic still compiles.</p><pre><code>CATEGORY_PIPELINE = {
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    "process_creation": "sysmon",
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    "process_access": "sysmon",
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    "image_load": "sysmon",
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    "file_event": "sysmon",
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    "network_connection": "sysmon",
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    "dns_query": "sysmon",
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}
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SERVICE_PIPELINE = {
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    "security": "windows-audit",
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    "system": "windows-audit",
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}
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def pipeline_for(rule: dict) -> str | None:
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    ls = rule.get("logsource", {})
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    if ls.get("product") == "windows":
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        if ls.get("category") in CATEGORY_PIPELINE:
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            return CATEGORY_PIPELINE[ls["category"]]
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        if ls.get("service") in SERVICE_PIPELINE:
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            return SERVICE_PIPELINE[ls["service"]]
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    return None</code></pre><p>Each rule then runs through <code>sigma convert -t &lt;backend&gt; -s</code>, with <code>-p &lt;pipeline&gt;</code> appended when one matched. A non-zero exit surfaces the last stderr line as the skip reason, so a broken rule is loud rather than silent.</p>
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<h2>All three backends</h2>
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<p>The LSASS source above compiles to each target without the logic being re-derived. The same selection plus the same exclusion list, expressed in three dialects. Splunk SPL:</p><pre><code>EventID=10 TargetImage="*\\lsass.exe" GrantedAccess IN ("0x1010", "0x1410", "0x143a", "0x1438", "0x1fffff") NOT (SourceImage IN ("*\\wininit.exe", "*\\csrss.exe", "*\\MsMpEng.exe", "*\\wmiprvse.exe"))</code></pre><p>Elastic ES|QL:</p><pre><code>from * metadata _id, _index, _version | where EventID==10 and ends_with(TargetImage, "\\lsass.exe") and (GrantedAccess in ("0x1010", "0x1410", "0x143a", "0x1438", "0x1fffff")) and not (ends_with(SourceImage, "\\wininit.exe") or ends_with(SourceImage, "\\csrss.exe") or ends_with(SourceImage, "\\MsMpEng.exe") or ends_with(SourceImage, "\\wmiprvse.exe"))</code></pre><p>Microsoft Sentinel / Defender KQL:</p><pre><code>EventID == 10 and ((TargetImage endswith "\\lsass.exe" and (GrantedAccess in~ ("0x1010", "0x1410", "0x143a", "0x1438", "0x1fffff"))) and (not((SourceImage endswith "\\wininit.exe" or SourceImage endswith "\\csrss.exe" or SourceImage endswith "\\MsMpEng.exe" or SourceImage endswith "\\wmiprvse.exe"))))</code></pre>
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<h2>Correlation rules</h2>
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<p>Single events are often informational; the alert is in the aggregate. SSH brute force is modelled as a Sigma correlation over a per-event base rule. The base rule is tagged <code>informational</code> and matches one failed authentication:</p><pre><code>title: SSH Authentication Failure
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name: ssh_auth_failure
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id: cc6fd1c9-b264-4be8-bb53-b6f4e2af9776
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status: experimental
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description: Base detection for a single failed SSH authentication, used by the brute-force correlation.
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logsource:
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  product: linux
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  service: sshd
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detection:
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  selection:
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    - Message|contains: 'Failed password for'
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    - Message|contains: 'Invalid user'
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    - Message|startswith: 'Connection closed by authenticating user'
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  condition: selection
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level: informational</code></pre><p>The correlation rule references that base by <code>name</code> and fires on eight or more failures from one source IP inside a two-minute window:</p><pre><code>correlation:
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  type: event_count
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  rules:
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    - ssh_auth_failure
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  group-by:
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    - src_ip
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  timespan: 2m
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  condition:
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    gte: 8</code></pre><p>Because a correlation needs its referenced rule present in the same collection, these are compiled together (<code>make correlations</code> converts the whole <code>linux/credential-access/</code> directory at once).</p>
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<h2>The test suite</h2>
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<p><code>pytest</code> gates rule quality before anything compiles. Schema, ATT&CK tagging, unique IDs, and correlation references are all enforced. The technique tag is matched against a regex and tactic tags against the full ATT&CK enterprise set:</p><pre><code>TECHNIQUE_RE = re.compile(r"^attack\.t\d{4}(\.\d{3})?$")
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@pytest.mark.parametrize("path", RULES, ids=[p.name for p in RULES])
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def test_rule_schema(path):
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    rule = load(path)
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    for field in ("title", "id", "status", "description", "tags", "level"):
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        assert rule.get(field), f"{path.name} missing {field}"
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    assert "detection" in rule or "correlation" in rule, f"{path.name} has no detection/correlation"
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    assert rule["level"] in {"informational", "low", "medium", "high", "critical"}
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    assert rule["status"] in {"experimental", "test", "stable", "deprecated", "unsupported"}
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@pytest.mark.parametrize("path", RULES, ids=[p.name for p in RULES])
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def test_attack_tags(path):
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    tags = load(path).get("tags", [])
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    techniques = [t for t in tags if TECHNIQUE_RE.match(t)]
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    assert techniques, f"{path.name} has no ATT&CK technique tag"</code></pre><p>The correlation check loads every rule's <code>name</code> and asserts that each correlation's references resolve, so a renamed or deleted base rule fails the build instead of silently producing an empty alert:</p><pre><code>def test_correlation_refs_resolve():
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    names = {load(p).get("name") for p in RULES if load(p).get("name")}
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    for path in RULES:
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        corr = load(path).get("correlation")
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        if corr:
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            for ref in corr.get("rules", []):
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                assert ref in names, f"{path.name} references unknown rule '{ref}'"</code></pre>
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<h2>CI workflow</h2>
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<p>Every push and pull request to <code>main</code> runs a single <code>validate</code> job that lints, tests, and compiles in order, then uploads the generated queries as an artifact. The trimmed workflow:</p><pre><code>jobs:
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  validate:
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    runs-on: ubuntu-latest
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    steps:
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      - uses: actions/checkout@v4
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      - uses: actions/setup-python@v5
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        with:
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          python-version: "3.11"
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      - run: pip install -r requirements.txt
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      - name: Lint Sigma rules
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        run: sigma check rules/
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      - name: Schema + ATT&CK tests
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        run: pytest -q
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      - name: Convert to Splunk / Elastic / Sentinel
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        run: python tools/convert.py
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      - uses: actions/upload-artifact@v4
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        with:
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          name: converted-queries
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          path: dist/</code></pre><p>A conversion error fails the job, so a rule that lints but does not compile to one of the three backends never merges. The <code>dist/</code> tree is gitignored and rebuilt from source on every run.</p>
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<h2>ATT&amp;CK coverage and validation</h2>
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<p>A focused, high-signal set covering the techniques that show up most in real triage: credential dumping, phishing-to-execution, persistence, and brute force. Nine techniques across five tactics, every rule tagged and tuned past the naive version.</p><table><thead><tr><th>Tactic</th><th>Technique</th><th>Detection</th><th>Platform</th><th>Level</th></tr></thead><tbody><tr><td>Initial Access</td><td>T1566.001</td><td>Office spawns scripting host / LOLBin</td><td>Windows</td><td>high</td></tr><tr><td>Execution</td><td>T1059.001</td><td>PowerShell EncodedCommand</td><td>Windows</td><td>medium</td></tr><tr><td>Execution</td><td>T1059.004</td><td>Reverse shell one-liner</td><td>Linux</td><td>high</td></tr><tr><td>Defense Evasion</td><td>T1218.011</td><td>Suspicious rundll32</td><td>Windows</td><td>high</td></tr><tr><td>Persistence</td><td>T1543.003</td><td>New service installed (7045)</td><td>Windows</td><td>high</td></tr><tr><td>Persistence</td><td>T1053.005</td><td>Scheduled task created (4698)</td><td>Windows</td><td>medium</td></tr><tr><td>Persistence</td><td>T1543.002</td><td>Systemd persistence</td><td>Linux</td><td>medium</td></tr><tr><td>Credential Access</td><td>T1003.001</td><td>Suspicious LSASS access</td><td>Windows</td><td>high</td></tr><tr><td>Credential Access</td><td>T1110</td><td>SSH brute force (correlation)</td><td>Linux</td><td>high</td></tr></tbody></table><p>CI proves the rules lint, pass their tests, and compile. Behaviour is validated separately: in a companion purple-team lab, Atomic Red Team fires each technique and the matching detection is confirmed in a Wazuh SIEM before a rule is promoted here. New detections are only added after they survive that loop, which keeps the set small, high-signal, and grounded in observed telemetry rather than copied from public rule dumps.</p>
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  <p class="repo-line">Repository &middot; github.com/zionboggan/detection-as-code</p>
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