Audit Trail Generation for Every Automated Maneuver Decision

For satellite operators, every automated maneuver decision carries liability. Insurers demand proof of due diligence; regulators require transparent rationale. A custom audit trail workflow automates the creation of an immutable, time-stamped record, capturing the precise input data (e.g., CDMs from SOCRATES, telemetry), the reasoning of collision probability models, the fuel-aware optimization logic, and the final decision trigger. This defensible package replaces fragile, manual log-keeping, directly reducing legal exposure and streamlining claims processing after a high-conjunction event. The architecture must integrate with SSA data pipelines, maneuver planning agents, and command systems.

Implementation requires a ledger service, like a blockchain-based datastore or a write-once system, integrated via API with your orchestration layer (e.g., LangGraph). Each log entry cryptographically chains to the previous, preventing tampering. The workflow must also include exception routing for data gaps or model low-confidence scores, forcing a human review gate. Post-maneuver, telemetry validation results are appended, closing the loop. This creates a continuous, court-ready evidence chain that satisfies ISO and regulatory standards for automated space operations, turning a compliance burden into a strategic asset.

For satellite operators, each automated collision avoidance decision carries significant liability. The custom audit trail workflow ingests and time-stamps every input—raw SSA data, covariance matrices, fuel states, and telemetry—before the AI model evaluates risk. It then captures the model's reasoning, the specific decision logic applied, and the final maneuver recommendation or command. This immutable assembly line transforms operational data into a legally defensible narrative, protecting against post-event disputes and satisfying stringent regulatory scrutiny from bodies like the FCC and NOAA.

Implementation requires integrating this ledgering layer directly with the maneuver orchestration stack, using cryptographic hashing for tamper evidence and a queryable database like TimescaleDB for time-series analysis. The workflow must include explicit gates for human-in-the-loop review on high-risk decisions, with all overrides and rationales captured. This architecture not only mitigates legal and insurance risk but also creates a rich dataset for post-event analysis, continuous model validation, and demonstrating operational due diligence to stakeholders.

For satellite operators and insurers, an automated maneuver without a detailed audit trail is an unacceptable liability. This custom workflow captures the complete decision chain: raw SSA data inputs, the reasoning of collision probability models, the trade-off analysis of fuel-aware planning agents, and the final authorization logic. The business value is direct: it creates a defensible package that satisfies FCC/NOAA reporting mandates, supports insurance claims in the event of a loss, and provides internal engineering teams with a searchable record for post-event analysis and system improvement, turning a regulatory burden into an operational asset.

Implementation is phased, starting with core logging to a tamper-evident database like Amazon QLDB or a blockchain layer, ensuring record integrity. Phase two integrates explainability tooling (e.g., SHAP for ML models) to capture 'why' the AI scored a risk as high. The final phase automates report generation against regulatory templates using a RAG-enhanced LLM agent, pulling from the ledger and approved clause libraries. Critical controls include cryptographic hashing of all inputs, mandatory human-in-the-loop approval gates for any burn, and immutable logging of all overrides or parameter changes to the AI system itself.

An automated audit trail transforms collision avoidance from a black-box decision into a legally defensible operational record. For each maneuver, the system must capture the raw conjunction data, the ingested satellite telemetry, the reasoning of the risk-scoring and optimization agents, and the final decision logic. This immutable log protects operators from liability, satisfies regulator inquiries from bodies like the FCC or NOAA, and provides insurers with the verified data needed for claims adjudication, turning a compliance burden into a risk-mitigation asset.

Implementation requires integrating the logging layer directly into the maneuver orchestration stack, using a write-once data store like an immutable ledger or versioned object storage. Each log entry is cryptographically signed and includes pointers to the exact software versions and model weights used. The architecture must support real-time querying for incident review and batch generation of compliance reports, ensuring the audit trail is both operationally useful and forensically sound under scrutiny from external auditors or litigation.