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HomeEngineering autonomous resilienceSystemsExplore systemsCapabilitiesExplore capabilitiesDefenseExplore defenseResearchExplore researchIndustriesExplore industriesCompanyExplore companyInsightsExplore insightsMission SystemsMission software connects fragmented observations, operational workflows and human decisions across distributed systems.Autonomous SystemsSoftware for UAS, drones, ground robotics, maritime and undersea systems: perception, planning, coordination and human supervisory control.Cyber OperationsDefensive engineering, authorized security testing and cyber research across software, identity, networks and infrastructure.Mission AIModels, retrieval, tools and agents organized around decision support, bounded action and operator oversight.

AI Assurance & Security / ENGINEERING SCOPE

Trust is an evaluated property.

Security boundaries and evaluation systems for models, tool-using agents and human-supervised AI.

Mission AI / authority architectureCONCEPTUAL MODEL

LAYER / 01

Data

Sources retain provenance, sensitivity and freshness. Untrusted content remains data, not authority.

Dependency: source integrity · Next boundary: Models

IdentityPolicySecurityObservabilityGuardrailsHuman control

01 / The technical problem

Design for the actual environment.

Agent authority crosses model, application and identity boundaries. Untrusted content must not acquire the privileges of an operator.

02 / Engineering scope

AI Assurance & Security

01

Adversarial evaluation

Test prompt-injection resilience, model robustness, adversarial ML and data integrity with controlled scenarios. Separate model behavior from permissions enforced by the application.

02

Secure agent execution

Apply scoped credentials, least privilege, sandboxing, validated arguments and human authorization at the action boundary. Treat multi-agent messages as untrusted inputs.

03

Assurance evidence

Connect model and agent telemetry to provenance, versioned evaluations and policy decisions. Model supply chains need artifact identity, dependency review and rollback plans.

03 / Architecture

Make the boundaries explicit.

  1. 01Identity
  2. 02Inputs
  3. 03Model
  4. 04Tool boundary
  5. 05Approval
  6. 06Audit

Operating constraints

  • Identity
  • Policy
  • Observability
  • Human control

Evaluation approach

Record reproducible failures and evaluate mitigations against held-out scenarios. A passing demonstration is not a certification of model safety.

Define requirements, interfaces and acceptance evidence before implementation. Instrument behavior, inject failure and preserve the record needed for review.

Machine / processing

IngestClassifyCorrelateSimulateRecommend

Human / authority

UnderstandPrioritizeJudgeAuthorizeCommand
DecisionMachines process. Humans command.

04 / Research connections

Questions beyond the implementation.

RESEARCH INTEREST

AI Assurance

↗RESEARCH INTEREST

Secure Agents

↗RESEARCH INTEREST

Cyber R&D

↗

Define the mission

Bring the hard problem.

Start with the operating environment, the constraints and the decision your system needs to support.

Discuss a program