Aerospace and Defense AI That Passes Verification, Not Just Demos
AI verification, edge inference, and autonomous systems architecture for defense programs and commercial aerospace operations.
Frequently Asked Questions
How do we get an AI-enabled system through a DoD milestone decision when CDAO T&E frameworks don't map to our TEMP?
The disconnect is real: CDAO's T&E strategy frameworks were designed for AI-specific evaluation, but most program TEMPs follow the traditional DT&E/OT&E structure that assumes deterministic system behavior. ML-based systems are probabilistic, and their performance changes with data distribution, operational conditions, and adversarial inputs. We bridge this by building AI-specific test plans that nest inside existing TEMP structures. This means defining operational performance envelopes (not just benchmark accuracy), documenting known failure modes and performance boundaries under realistic conditions, and producing the verification evidence in formats that satisfy both the CDAO AI Assurance team and the program's operational test agency. The goal is a test package that demonstrates responsible AI compliance without requiring the milestone decision authority to become an ML expert.
What does the CMMC 2.0 AI security framework require for defense AI contractors?
The FY2026 NDAA imposed AI-specific security controls on defense contractors beyond baseline CMMC 2.0 requirements. There are four mandated control categories: data input validation and sanitization to prevent prompt injection attacks, model access controls with multi-factor authentication and role-based permissions, comprehensive AI output monitoring and logging integrated with SIEM systems, and adversarial attack prevention measures including continuous model validation and defensive testing. The DoD must provide a status update to Congress by June 16, 2026, which will clarify implementation timelines. The biggest compliance risk right now is shadow AI: undocumented usage of commercial AI tools by cleared personnel on networks connected to CUI. Commercial AI services typically operate in standard cloud environments that do not meet CUI processing requirements under DFARS 252.204-7012. We build compliance architectures that provide sanctioned AI capabilities within CMMC boundaries while implementing monitoring to detect and redirect unsanctioned usage.
How do we deploy cloud-trained ML models on SWaP-constrained tactical platforms?
The pipeline from cloud model to fielded edge model has four stages, each with trade-offs that affect operational performance. First, model optimization: quantization from FP32 to INT8 or INT4 reduces compute requirements but introduces accuracy degradation that varies by model architecture and task. The differences between post-training quantization methods (GPTQ, AWQ, GGUF) are meaningful for both inference speed and output quality on target hardware like Jetson Orin or Qualcomm RB5. Second, architecture pruning and knowledge distillation to reduce model size while preserving task-critical performance. Third, thermal and power profiling: inference generates heat that can degrade performance by half in enclosed platforms operating in harsh environments. Fanless or liquid-cooled architectures add weight and complexity. Fourth, validation under MIL-STD-810H environmental conditions to verify that the optimized model meets performance requirements across the operational envelope, not just on a bench. We build this pipeline as an integrated MLOps workflow that operates within classified network boundaries where standard open-source tooling is restricted.
Are AI model outputs considered ITAR-controlled technical data?
This is one of the most consequential unresolved compliance questions in defense AI right now. ITAR's definition of 'technical data' covers information required for design, development, production, or operation of defense articles, regardless of how that information was produced. If an LLM generates specifications, performance data, or engineering analysis for a USML-listed item, the output likely meets the definition of controlled technical data. The State Department's DDTC and Commerce Department's BIS have not issued authoritative guidance specifically addressing AI-generated technical data. In the absence of guidance, the conservative interpretation is that AI outputs describing defense articles are controlled. This creates practical problems: an engineer using a commercial LLM to draft a technical document may be creating a deemed export if the model processes the prompt on servers outside the US. Cloud storage, collaboration tools, and AI prompts can all trigger unauthorized exports. We build ITAR-compliant AI development workflows that keep controlled data within authorized boundaries, using on-premise or GovCloud inference endpoints and implementing data classification controls that prevent inadvertent exports through AI tool usage.
How do we validate autonomous drone navigation in GPS-denied contested environments?
GPS denial is the baseline operating assumption for near-peer contested environments, not an edge case. Autonomous navigation alternatives span inertial measurement (where MEMS IMU drift accumulates mission-limiting errors within minutes), visual-inertial odometry (where lighting, weather, and featureless terrain degrade feature matching), and terrain-relative navigation (where database accuracy varies by region). Each modality has failure modes that compound when fused without rigorous validation. The critical insight is that you cannot validate GPS-denied performance by simply turning off GPS on a test range. You need to test against specific jamming waveforms, spoofing scenarios, and environmental conditions matching the operational theater. We build verification frameworks that test sensor fusion resilience under realistic denial scenarios, validate navigation accuracy against specific EW threat profiles, and verify that autonomous decision-making stays within mission parameters when primary navigation inputs are degraded or deceptive. The output is the evidence base that a test director needs for an operational evaluation and a program manager needs for a milestone decision.
What's the real difference between Anduril Lattice and Palantir AIP for defense AI programs?
They solve different problems. Palantir AIP and TITAN are data integration and targeting platforms: they fuse sensor data from multiple sources into an operational picture and support human decision-making. TITAN specifically focuses on deep-sensing intelligence fusion for targeting, with the first prototypes delivered to the 1st Multi-Domain Task Force in March 2025. Anduril Lattice is a mission autonomy platform: it manages heterogeneous autonomous systems across domains and enables collaborative autonomous operations in degraded environments. The $20 billion Army contract consolidates everything from sensors to drones to cloud infrastructure. The distinction matters for procurement strategy. A program that needs AI-enabled intelligence fusion and targeting support is evaluating Palantir's stack. A program that needs autonomous system coordination across a fleet of unmanned assets is evaluating Anduril's stack. A program that needs both has an integration challenge that neither vendor is incentivized to solve neutrally. We provide vendor-neutral technical assessment that evaluates platforms against your specific mission requirements rather than vendor roadmaps.
How do we build predictive maintenance AI for a mixed commercial aircraft fleet?
The core challenge is data heterogeneity. A mixed fleet generates sensor data in different formats, at different sampling rates, from different generations of avionics and health monitoring systems. Airframes that entered service in the 1990s have fundamentally different instrumentation than those delivered last year. Building a predictive maintenance model that works across the fleet requires a data integration layer that normalizes heterogeneous sensor inputs, domain-specific feature engineering that maps raw sensor readings to known degradation patterns for each aircraft type, and physics-informed modeling that incorporates manufacturer maintenance intervals and failure mode data rather than relying purely on statistical patterns. The validation requirement is critical: a model that predicts a component failure must be tested against ground-truth maintenance records to verify it is generating actionable warnings rather than false alarms that erode maintenance crew trust. Global commercial MRO demand is growing at 3.2% CAGR through 2035, and the operators who capture value will be those whose AI reduces unscheduled maintenance events, not those who deploy AI dashboards that maintenance teams learn to ignore.
How do we test agentic AI for military decision support without creating overtrust?
The DoD's January 2026 AI strategy calls for an 'Agent Network' spanning battle management, decision support, and kill chain execution. Simultaneously, growing research shows that agentic systems absorb corrections or resist assessments in ways that operators cannot observe, and humans tend to overtrust AI systems that appear authoritative. Anthropic publicly stated concerns about using AI models for compiling targeting lists without validated reliability. Testing agentic military AI requires a fundamentally different approach than testing a standalone model. We build evaluation frameworks that test agent behavior under degraded information conditions (not just optimal scenarios), measure whether human operators actually override agent recommendations when the agent is wrong versus deferring to its apparent confidence, and validate that agent reasoning chains are transparent enough for after-action review. The goal is an evaluation that reveals whether the human-machine team performs better than either alone, or whether the agent creates the 'illusion of control' that safety researchers have identified as the core risk of military agentic AI.
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Veriprajna Deep Tech Consultancy specializes in building safety-critical AI systems for healthcare, finance, and regulatory domains. Our architectures are validated against established protocols with comprehensive compliance documentation.