Sigma and KQL describe patterns in structured event logs — file writes, network connections, authentication events. AI behavior doesn't produce structured events. It produces probabilistic outputs, semantic reasoning chains, tool invocation sequences, and agentic decision trees. None of those surface in a log aggregator in any form a traditional rule can key on. If you're trying to detect prompt injection or agent hijacking by writing KQL against your SIEM, you're not detecting the behavior — you're detecting the log noise around it, if anything shows up at all.

Starseer takes a different approach entirely: we use YARA rules for AI detection engineering. YARA is already the language security teams trust for expressive, precise pattern matching — and Starseer gives you a telemetry surface that YARA can actually operate against for AI behavior. The platform normalizes AI signals — prompt content, inference outputs, tool invocation sequences, decision path traces, output distribution shifts — into structured, inspectable records that your YARA rules run against directly.

The result is that you're writing in a rule language your team already knows, with the tooling and review practices you've already built around it, against an attack surface that no SIEM or traditional EDR can reach. You're not learning a proprietary detection schema or rebuilding your institutional knowledge from scratch. You're extending an existing practice into a new layer — the one that your current stack is blind to.

Yes. Starseer exposes a CLI and API that integrate with GitHub Actions, GitLab CI, Jenkins, and most standard pipeline tooling. Detections are tested on every push, and deployments are gated on pass/fail status — the same way a failing unit test blocks a code merge.

You configure the quality thresholds per detection or per rule set: minimum true positive rate, maximum false positive rate, required ATLAS technique coverage assertions. Starseer enforces them automatically. Rules that don't meet threshold don't ship. There's no manual sign-off bottleneck and no way for an under-tested detection to reach production without someone explicitly overriding the gate and leaving an audit trail.

The library is a curated set of AI attack fixtures mapped to the MITRE ATLAS technique set. Current coverage includes prompt injection variants — direct, indirect, multi-turn, base64-encoded, and multilingual — agent hijacking patterns, LLMjacking resource abuse behaviors, data poisoning trigger signatures, model tampering indicators, and covert agentic workflow patterns.

Each fixture is a structured test case: an input condition, an expected firing behavior, and a set of contextual modifiers. You reference fixtures directly in your detection's test block, or run the full library against a detection to measure coverage breadth. The library is updated continuously as new attack patterns are documented — your existing detection tests automatically gain new coverage assertions without you rewriting any rules. A detection that passed last month can surface a new gap this month if a new fixture exposes it, which is exactly the behavior you want.

This is the most common way detection programs deteriorate — suppressions accumulate, nobody audits them, and the rule that looked like it had 90% coverage six months ago is now effectively firing on 40% of the surface it was designed for. Starseer is built to prevent this specifically.

Every suppression is a versioned artifact attached to a specific detection rule. It requires a written rationale, a defined scope, and an expiration date. Suppressions cannot silently expand — scope changes require a new commit. On every suppression commit, Starseer generates a coverage diff report showing the net coverage impact: what you're choosing not to fire on, and how much of the intended detection surface that represents. Expired suppressions are flagged automatically and surface in your next pipeline run as warnings. You always have an auditable record of every exception, when it was made, why, and whether it's still intentional.

Two mechanisms. First, historical event replay: Starseer lets you run any detection against a window of real production telemetry captured by AI-EDR, so you can measure how a rule would have performed against actual observed behavior — not simulated inputs. This surfaces false positive rates that unit tests miss because production inputs are messier and more varied than anything you'd construct by hand.

Second, the adversarial simulation library is continuously updated to reflect real-world attack patterns as they're documented in the wild. A detection that passes today's fixtures is being tested against techniques that have actually been observed, not just theorized. Between replay against real data and simulation against real attack patterns, you get a materially more honest picture of production performance than any pure unit testing approach can give you. Neither replaces the other — the strongest validation posture uses both.

Yes, and there are good reasons to use either without the other. AI-DE alone gives you a detection engineering workbench — you can build, test, and maintain a detection library without a live AI-EDR deployment. Teams that already have runtime monitoring infrastructure sometimes use AI-DE specifically to improve the quality of detections they push to other platforms.

AI-EDR alone gives you runtime monitoring and response using Starseer's built-in detection library — fully functional without custom rule authoring.

That said, the two products compound significantly when used together. AI-EDR generates the production telemetry that feeds historical replay in AI-DE. Detection quality metrics from AI-DE inform tuning decisions in AI-EDR. False positive signals from production flow back into suppression management in the engineering workflow. The loop between production signal and detection improvement is where most of the long-term value accumulates — closing that loop manually, across two separate platforms, is exactly the friction AI-DE and AI-EDR are designed to eliminate together.

It does, but that's a side effect of doing the engineering work well — not a goal you should be optimizing for directly. If your detections are high-quality, versioned, tested against documented attack techniques, and shipped through a governed pipeline, the compliance artifacts generate themselves: ATLAS technique mappings, test result histories, coverage reports, suppression audit trails, and deployment logs.

Starseer surfaces these as exportable reports aligned to NIST AI RMF, ISO 42001, and the EU AI Act. The framing we'd suggest to auditors and regulators is not "we have a compliance tool" — it's "our detection engineering practice produces evidence of control effectiveness as a natural output." That's a materially stronger position than a dashboard that generates PDFs on demand, and it holds up to scrutiny in a way that after-the-fact reporting typically doesn't.