AI Hallucination in Regulatory Data: The Trust Problem

When I first connected an LLM to Orbit Sentinel’s regulatory database, the goal was straightforward: surface structured data extracted from filings across agencies, verify claims against source documents, and tell the story behind each stage of a satellite application.

Things went wrong when I was verifying the output and noticed an acronym I didn’t recognize. I looked it up. It didn’t exist.

That red flag led to extensive testing, and more problems surfaced: fabricated dates on real filings, false claims of “first-ever” achievements, baseless use of “unprecedented.” Worst of all, I caught one model using real filings and real entities but inventing a narrative to connect them, a story that sounded authoritative but had no basis in the underlying records.

I shifted from building to triage. I designed a six-prompt test with response scoring and expected results, ran it across multiple LLMs to find the right quality fit, and went hunting for every data gap and server bug I could find. The initial score was 0.5 out of 6. The hardest part wasn’t the engineering. It was accepting that the pipeline I’d built needed to be torn apart and reassembled with verification at every stage.

I implemented eight anti-hallucination rules, added data provenance tracking and confidence scoring across the pipeline, and after days of tuning, the results showed it: 5.5 out of 6, with a clear map of the remaining data gaps to fill.

I’m sharing this because in space regulatory intelligence, the stakes are too high for plausible-sounding answers. Trusting your data is only half the problem. You also need to know that the narrative built from that data is grounded in fact, not fabrication.

The AI Plausibility Trap

Regulatory data is particularly vulnerable to AI hallucination, the generation of plausible but false information, because it looks structured. Filing numbers follow predictable formats. Docket identifiers have consistent patterns. Entity names, frequency bands, orbital parameters. All of it has the appearance of precision. An AI system can fabricate a plausible FCC filing number or ITU satellite network designation that would pass a casual review.

The problem compounds because regulatory data is verifiable but rarely verified. The analyst reviewing an AI-generated summary of recent FCC activity is unlikely to cross-reference every filing number against the Electronic Comment Filing System. The investor evaluating a competitive intelligence report probably isn’t pulling up ITU Space Network List records to confirm that the satellite networks mentioned actually exist.

This creates a dangerous dynamic: the more professional the output looks, the less scrutiny it receives. And AI is very good at producing professional-looking output. Research from Stanford and other institutions has shown that large language models hallucinate at significant rates that increase with domain complexity, and niche regulatory domains with limited training data are particularly vulnerable.

The consequences are concrete. If a hallucinated filing shows up in a due diligence report, the firm is exposed. Get an entity relationship wrong in a competitive intelligence briefing, and someone makes a strategic decision based on fiction. And if a spectrum coordination record gets misattributed, an operator could underestimate interference risk in a frequency band they’re planning to use. These aren’t hypotheticals. I’ve seen the raw material for every one of them come out of an LLM that had access to real data.

Three AI Failure Modes

Hallucinations vary in severity, and in regulatory intelligence they tend to fall into three categories. The most dangerous ones aren’t the most obvious.

Fabricated Records

Fabricated records are the failure mode people think of first: the AI generates a filing, entity, or regulatory action that simply doesn’t exist. During early testing of Orbit Sentinel’s MCP server, semantic search results returned what looked like real satellite applications for SpaceX Gen2, OneWeb Phase 2, and Telesat Lightspeed. They weren’t real. They were leftover seed data from development that had been embedded alongside production records. An LLM consuming those results would have cited them as fact. In a separate test, the model knew the full name of the Consortium for Execution of Rendezvous and Servicing Operations but fabricated the acronym “CERSO” instead of the real one, CONFERS. It sounded right. It wasn’t.

Misattribution

Misattribution is subtler. The data is real, but the context is wrong. In one test, the system returned two EchoStar-related dockets and the LLM reported they were “filed on the same day.” The actual filing dates were August 26 and September 8, neither matching what the model claimed. In another case, the model found zero prior Blue Origin filings in our ECFS data and concluded it was Blue Origin’s “first-ever FCC filing.” It wasn’t. Blue Origin has held FCC experimental licenses since 2017 through IBFS, a data source the system didn’t cover. Each individual fact checked out in isolation, but the conclusions drawn from them were wrong.

Narrative Fabrication

Narrative fabrication is the hardest to catch. The data points are accurate, but the story connecting them is invented. When I asked the MCP server for notable recent filings, the LLM didn’t just return records. It told a story. It found filings with similar dates and narrated that they were “filed on the same day,” implying coordination. It added phrases like “this signals” and “the timing indicates,” generating analytical inference that was indistinguishable from retrieved facts. By the time the output reached me, I couldn’t tell where the data ended and the storytelling began. That’s what makes this failure mode dangerous. The model is optimized to produce coherent narrative, and coherent narrative is exactly what an analyst expects to see.

Most organizations worry about fabricated records. They should worry more about narrative fabrication, because it’s difficult to detect without systematic verification against source systems.

Why Space Regulatory Data Is Uniquely Vulnerable

AI hallucination is a problem everywhere, but space regulatory data has structural properties that make it worse than most domains.

There is no single source of truth. Satellite licensing data is fragmented across the FCC (which itself splits filings across IBFS, ECFS, and ULS), the ITU (Space Network List, Master International Frequency Register), the FAA (AST launch and reentry licensing), and NOAA (remote sensing licenses). Each agency uses its own identifier formats. The FCC tracks entities by FRN and callsign, the ITU uses network notations, the FAA uses its own license numbering scheme. An AI system stitching together information across these sources has abundant opportunity to cross-wire identifiers, misattribute filings, or fabricate connections between records that share no actual relationship.

Most of these systems lack public APIs. Data extraction means scraping PDFs, parsing HTML tables, and interpreting documents that range from structured forms to free-text narratives. The raw data quality varies by agency, by filing type, and sometimes by year. This is the kind of messy, inconsistent input where AI systems are most likely to fill gaps with plausible invention rather than flagging uncertainty.

The domain is also small relative to what LLMs have been trained on. There are orders of magnitude more legal opinions and SEC filings in any model’s training data than there are space station applications or spectrum coordination records. When a model has seen fewer examples of a domain, it’s more likely to hallucinate within it, generating outputs that pattern-match to what regulatory data looks like without being grounded in what it actually says.

What Other Industries Learned

Space regulatory intelligence isn’t the first high-stakes domain to confront AI trust. Legal technology solved citation verification decades ago with citator systems like Shepard’s Citations and KeyCite. Every case citation can be checked against a canonical database. The Mata v. Avianca debacle happened not because the tools didn’t exist, but because the attorneys bypassed them.

Financial compliance relies on audit trails and source-of-record requirements. Every data point in a regulatory filing to the SEC or a stock exchange is traceable to a primary record. The verification infrastructure is built into the workflow, not bolted on after the fact.

Space regulatory intelligence has neither. No citator for filings across agencies. No canonical cross-agency database. No industry-standard verification layer. The tools that do exist are agency-specific and siloed, useful for checking individual records but not for validating the cross-agency relationships and narratives that AI systems generate. This gap is where the hallucination risk concentrates, and where the opportunity lies. It’s what we’re building with Orbit Sentinel.

The Safeguards That Matter for Regulatory AI

Solving this isn’t a matter of prompting AI more carefully or adding a disclaimer to generated outputs. It requires architectural discipline: a set of engineering constraints that make hallucination structurally difficult rather than merely discouraged.

Data Provenance

Every claim surfaced by a regulatory intelligence platform must trace to a source record. Not a summary of a source record. Not a paraphrase. The actual filing, with its identifier, date, and originating agency. If the system can’t point to the source, it shouldn’t surface the claim.

Structural Verification

Identifiers, dates, entity names, and regulatory references should be validated against source systems at the point of extraction, not at the point of presentation. A filing number that doesn’t resolve to a real record in the source database should be rejected before it ever reaches an analyst, not flagged after the fact.

Confidence Boundaries

When extraction confidence is low (because a document is poorly formatted, a field is ambiguous, or a reference is incomplete) the system should say so explicitly. Filling gaps with plausible inferences is a direct path to hallucination. Uncertainty, clearly communicated, is more valuable than false precision.

Separation of Data and Narrative

The system that stores and retrieves regulatory filings must be architecturally distinct from the system that generates analysis or insights. When the data layer and the narrative layer are entangled, there’s no way to audit where facts end and interpretation begins. This separation isn’t just good engineering. It’s the only way to make AI-generated analysis verifiable.

What We’re Building: Verifiable Analysis

I built verification into Orbit Sentinel because without it, the system is just a storyteller. Any AI-assisted insight needs to be independently auditable against the source records that produced it, the actual filing, retrievable by ID, with provenance intact. That standard will define every platform operating in high-consequence regulatory domains. The firms that get there first will set the benchmark everyone else has to meet.

Orbit Sentinel enforces cross-reference verification across the regulatory data pipeline. Every data point, whether it’s a filing, an entity relationship, or a spectrum allocation, traces to its source record, with confidence scoring that flags extraction uncertainty rather than concealing it. Data coverage is expanding across additional agencies and filing types, because incomplete data creates blind spots, and blind spots are where hallucinations hide.

The space industry’s regulatory complexity is accelerating. AI is a powerful tool for making sense of it at scale, but only if you can verify what it tells you. That’s what we’re building. Request early access to see it in practice, learn how we benchmark extraction quality, explore the full U.S. regulatory landscape, see how satellite licensing actually works, or read about the FCC’s 5-year deorbit rule.