Discover the exposure
Inventory AI services, connected tools, data classes, model access and the network paths that carry them.
AI Security
Secure the intelligence moving through your network.
A customer-facing program for AI-connected terrestrial and celestial operations. Start with the value of the service, identify the systems and data it depends on, then test the controls before scaling.
The architecture
One security view from constellation to enterprise.
We scope the path from spacecraft and ground gateways through fiber, cloud, AI services and user decisions. Each boundary has an owner, evidence, an allowed action and a recovery path.
Contain agent authority
Limit identities, tool permissions and data access. Require human approval for changes to live network or security controls.
Test realistic failure
Exercise prompt injection, unsafe tool calls, data leakage and service degradation in an isolated test environment.
Prove recovery at scale
Measure alerts, response time, fallback transport and audit evidence across sites, regions and growing device counts.
OpenAI options
Use published defensive tools where they fit.
These are options to evaluate with the customer; they are not active on a customer repository or network until the customer approves the connection and scope.
Codex Security Cloud
OpenAI announced repository scanning on demand or on a schedule, ongoing checks of new commits, investigation, duplicate reduction and prepared fixes. A defender reviews findings and proposed changes. Availability depends on an eligible OpenAI plan and authorized GitHub connection.
Daybreak Blue
OpenAI's defensive cybersecurity models can support vulnerability investigation, secure review, incident analysis and patch validation. Codex Security Cloud includes access to models offered through Daybreak Blue; use remains subject to OpenAI access and permitted defensive scope.
Private Safety Processing
For eligible API use cases, OpenAI describes Zero Data Retention with Private Safety Processing. We evaluate the customer's data requirements, eligibility and configuration before proposing it. Private Inference was announced as a future preview, not as a deployed feature here.
Human-reviewed code changes
Codex code review can help a team examine proposed changes. Findings and fixes flow through the customer's own review, tests and change approval; no model receives blanket authority to modify production infrastructure.
Start with a pilot
Choose a test that creates usable evidence.
A pilot begins in a local or otherwise approved test environment. Its output is a bounded recommendation, not a claim that the live network has been scanned.
AI-connected gateway boundary
Map gateway-to-AI data flows, test least privilege and approval gates, and record a response to blocked requests.
Repository defense review
With explicit repository authorization, evaluate Codex Security Cloud findings and proposed fixes against the customer’s own change process.
Constellation operations scenario
Simulate ground-station and Star Jumper telemetry workflows, failure handling and human escalation without connecting to live flight systems.
Scaling and resilience
Repeat the approved scenario across additional gateways and regions; measure latency, detection, recovery and evidence quality.
Shared program
Continue across the three customer sites.
Youniverse1 covers network and gateway boundaries. Galaxity AI covers AI systems and agent governance. Enterprise Horizon covers enterprise assessment, pilots and operating controls.
Status: service architecture and pilot options. No customer system or repository is connected from this page. OpenAI product availability and terms are controlled by OpenAI.