DGCA compliance for flight data programs: what auditors actually look for

If you operate a fleet under DGCA jurisdiction above the thresholds defined in CAR Section 5 Series F, you run a flight data monitoring (FDM) program. The requirements are real, the public CARs and advisory circulars define them in detail, and the audits happen. We’re not the right source for the regulatory text — that’s DGCA’s job, and the documents are publicly available on dgca.gov.in.

What we can describe is the operational shape of an FDR-related audit, distilled from building Aviation AI Pro for this market and from conversations with safety officers at small to mid-size Indian carriers. This is the practical shape of compliance, not a citation guide.

What inspectors check

Every audit conversation we’ve heard about lands on the same four questions:

1. Can you produce flight data for a specific flight in a specific window? If the inspector says “show me the data from VT-XYZ’s 14 March 2026 sector,” they want the actual decoded parameters — not just that the data exists somewhere, but the values, the time series, the validation status. Operations that store FDR files on shared drives without a search index discover this is harder than expected.

2. Can you show that your program is finding exceedances, not just collecting data? FDM exists to identify safety trends. The inspector will ask for the list of flagged exceedances in a defined period, the criteria used to flag, and what was done in response. Programs that have only the raw data without any analytical layer fail this question.

3. Can you show the chain from raw data to safety action? When a high-rate-of-descent exceedance fires, what happened next? Who reviewed it, when, what was the disposition, was the crew briefed, was a finding raised? This is the audit trail of the safety management system, with FDM as one input. Most small operations have the analytical layer but not the closure tracking.

4. Can you produce evidence that you can do all of the above on demand? This is the meta-check. The inspector wants to see that the system isn’t just hypothetically able to produce these answers — it actually does, regularly, with a routine that doesn’t depend on one specific person being available.

The four gap patterns

The gaps we see most often, in roughly the order of how often they catch operations:

Gap 1: Data is stored, not decoded. Operations dutifully receive FDR downloads from their fleet, archive them, and never decode them past the vendor’s bundled summary report. When asked for specific parameter time-series, they have to send the file back to the vendor or scramble to find someone with a decoder. The data exists; the program around it doesn’t.

Gap 2: Exceedance criteria are stale. The exceedance rules were set up at program inception and never tuned. Either they’re too permissive (no flags ever fire, the program looks idle) or too aggressive (every approach flags something, the safety officer ignores them all). DGCA inspectors do read the criteria during audits and ask why they were set where they’re set.

Gap 3: Closure tracking is in a separate system or not at all. The FDM system flags exceedances; the response lives in email or a spreadsheet or “we had a chat with the crew.” There’s no link from the flagged event to the closure record. Inspectors specifically ask for that linkage.

Gap 4: Single-person dependency. The entire FDM program runs because one safety officer knows where everything is. If that person is unavailable, the program is essentially inoperable. Inspectors flag this as a continuity risk during audits and increasingly call it out as a finding.

What an audit-ready FDR program looks like

The operational shape that actually survives audits, regardless of size:

• Indexed storage of decoded data. Every flight is decoded as it lands, the parameters are stored in a queryable form, and “show me VT-XYZ on 14 March 2026” is a query, not a paper hunt. Raw files are kept for the retention period but normal queries don’t depend on opening them.

• Configurable exceedance criteria with a review trail. The criteria are documented, dated, reviewed annually (or after any incident), and the version history is visible. When an inspector asks “why this threshold,” there’s an answer.

• Exceedances linked to actions. Every flagged event has a state — open, under review, briefed, closed — and a record of who did what when. The chain from “data showed X” to “we did Y about it” is one query.

• Reports generated on a schedule, not on demand. Monthly safety summaries, quarterly trend analyses, annual reviews — all produced automatically, signed off by the safety officer, retained. When the inspector asks “show me your last quarterly review,” it’s a click, not a project.

• A second operator who can run the program. Continuity isn’t optional. Either a second safety officer or a clear handover procedure (with the system designed to support handover).

What we built

Aviation AI Pro is built for exactly the gap most Indian carriers fall into: they have the regulatory obligation, they don’t have the tooling, and they can’t justify the price tag of European-carrier FDM systems.

The platform decodes flight data as it lands (ARINC 717 parser with Honeywell DLU support out of the box), runs configurable exceedance criteria, links each flagged event to a disposition record, and generates audit-ready reports on a schedule. Every parameter we show has provenance back to the source bytes — when an inspector asks “where did this number come from,” the answer is queryable.

If you’re running an FDM program at a smaller Indian carrier and the next audit is closer than you’d like, we’d be glad to talk.

The audit will check that you have a program. The audit will also check that the program works. The tooling matters because most of the gap between “has a program” and “program works” is one of plumbing.