Telecom AI Compliance: 5G, RAN, and Network Edge Attestation

1. Why Telecom AI Needs Independent Attestation

Modern radio access networks run AI at every layer. Beamforming optimization, spectrum allocation, traffic prediction, network slicing, and anomaly detection all rely on machine learning models that make millions of decisions per second across thousands of cell sites. These decisions directly affect service quality for subscribers and, in the case of emergency call routing, human safety.

Three properties of telecom AI create unique compliance challenges:

• Sub-millisecond latency constraints. Near-Real-Time RIC (Near-RT RIC) AI functions operate within a 10ms-1s control loop. Some physical layer AI decisions (beamforming, scheduling) execute in under 1ms. Traditional compliance logging cannot operate at this speed without affecting service.
• Subscriber data sensitivity. AI models that optimize per-user experience process subscriber identifiers, location data, and usage patterns. These are personal data under GDPR and the ePrivacy Directive. The AI system must be witnessed without exposing the data it processes.
• Critical infrastructure classification. EU AI Act Article 6(2) with Annex III classifies AI systems used in the management and operation of critical digital infrastructure as high-risk. Telecommunications networks are explicitly listed. This triggers the full Article 9-15 obligation set: risk management, data governance, technical documentation, logging, human oversight, accuracy, robustness, and cybersecurity.

SWT3 addresses all three. The protocol witnesses AI decisions by recording cryptographic hashes of model behavior, not the data itself. Clearing levels control what metadata is captured, and the write-ahead log pattern supports asynchronous attestation that does not block real-time inference paths.

2. O-RAN Architecture and Attestation Points

The O-RAN Alliance architecture disaggregates the RAN into functional components, each of which may host AI/ML functions. SWT3 attestation points map to where AI decisions are made:

Component	AI Functions	Control Loop	SWT3 Attestation
Non-RT RIC	Policy optimization, model training, A1 policy generation, network planning	> 1 second	Standard witness (AI-INF.1). Full metadata capture. Clearing Level 1-2.
Near-RT RIC	RAN slicing, load balancing, interference management, QoS optimization, xApps	10ms - 1s	Async witness via WAL. Level 0-1. Flush on configurable interval.
O-CU (Central Unit)	Handover prediction, bearer management, PDCP optimization	10ms - 100ms	Async witness. Level 0 for real-time path. Batch replay at Level 1.
O-DU (Distributed Unit)	Scheduling, beamforming, power control, HARQ optimization	< 1ms	Level 0 only. Numeric factors (3 integers). Post-hoc batch at Level 1.
O-RU (Radio Unit)	Digital front-end optimization (limited AI currently)	< 0.25ms	Hardware attestation only (AI-HW.1). No inference witnessing at this layer.
SMO (Service Management)	Lifecycle management, fault prediction, capacity planning	Minutes - hours	Full witness. All clearing levels. Standard SDK integration.

Attestation Flow

O-RU                O-DU               O-CU             Near-RT RIC         Non-RT RIC / SMO
[AI-HW.1 only]  ->  [L0: WAL]      ->  [L0/L1: WAL]  ->  [L1: async]     ->  [L1-2: standard]
                     3 factors           batch replay       xApp witnesses      full SDK integration
                     ~200 bytes          enriched flush     10ms-1s loop        rApp/A1 policies
                     no network I/O      60s interval       configurable        minutes-hours cycle

The critical design point is that attestation granularity decreases as you move down the stack toward real-time processing. The O-DU operates at physical layer speeds where even microseconds of overhead affect throughput. SWT3 handles this through clearing level selection: Level 0 captures only three numeric factors and a timestamp, adding negligible overhead. Richer attestation happens asynchronously via the write-ahead log.

3. Python SDK Quick Start for RAN AI

SWT3 provides two integration patterns for RAN AI. Choose the one that matches your inference architecture.

Pattern A: Direct Witness Methods (custom inference pipelines)

For RAN AI models served via gRPC, ONNX Runtime, TensorRT, or custom REST endpoints, use the dedicated witness methods. Each method maps to a specific SWT3 procedure and is non-blocking.

from swt3_ai import Witness

witness = Witness(
    tenant_id="your-tenant-id",
    api_key="axm_YOUR_KEY",
    agent_id="near-rt-ric-xapp-beam-01",
    clearing_level=0,           # Real-time path: numeric factors only
    flush_interval=60.0         # Batch flush every 60 seconds
)

# Your existing beamforming inference (unchanged)
beam_result = beam_model.predict(cell_measurements)

# Witness the decision -- one call, non-blocking
anchor = witness.witness_hardware(
    accelerator_count=4,
    health_status=1,
    silicon_vendor="arm-neoverse",
    discovery_method="pci-fallback",
    topology="o-du-cluster"
)
print(anchor.fingerprint)  # e.g., "96b7d56c0245"

# Witness model drift against production baseline
drift_anchor = witness.witness_drift(
    metrics_evaluated=8,
    drifted_count=0,
    drift_type="performance",
    baseline_hash="sha256:a1b2c3d4",
    drift_score=0.002,
    detection_method="mse",
    threshold=0.005
)

# Witness model weights at deployment
weights_anchor = witness.witness_model_weights(
    param_count=7_000_000,
    quantization_bits=8,
    weights_hash="sha256:e4f5a6b7..."
)

Direct witness methods work with any inference framework. Your model code does not change. The witness call appends to a local buffer and returns immediately.

Pattern B: Transparent Proxy (OpenAI-compatible model servers)

For RAN AI models served via OpenAI-compatible APIs (vLLM, Triton with OpenAI frontend, or LLM-based network assistants), the wrap() method provides zero-code-change witnessing:

import openai
from swt3_ai import Witness

witness = Witness(
    tenant_id="your-tenant-id",
    api_key="axm_YOUR_KEY",
    agent_id="non-rt-ric-rapp-01",
    clearing_level=1
)

# Wrap the model server client -- every call is witnessed transparently
client = openai.OpenAI(base_url="http://ran-llm-server:8080/v1")
witnessed_client = witness.wrap(client)

# Use exactly like the original -- witnessing happens automatically
response = witnessed_client.chat.completions.create(
    model="network-advisor-v2",
    messages=[{"role": "user", "content": "capacity forecast for sector 7"}]
)

Subscriber-Aware AI (Level 2)

For AI that processes per-subscriber data (QoS optimization, location services), use Level 2 with Cross-Jurisdictional Transparency (CJT) fields:

witness_sensitive = Witness(
    tenant_id="your-tenant-id",
    api_key="axm_YOUR_KEY",
    agent_id="near-rt-ric-xapp-qos-01",
    clearing_level=2,           # Sensitive: subscriber data involved
    jurisdiction="EU",          # GDPR jurisdiction
    legal_basis="legitimate_interest",
    purpose_class="network_optimization"
)

# Your existing QoS inference (unchanged)
qos_result = qos_model.optimize(subscriber_context)

# Witness the decision with CJT metadata
anchor = witness_sensitive.witness_performance(
    metrics_evaluated=5,
    metrics_passing=5,
    benchmark_type="qos_sla",
    score=0.97,
    threshold=0.95
)

The jurisdiction, legal_basis, and purpose_class fields survive all clearing levels. They provide auditors with the legal context for subscriber data processing without exposing the data itself.

4. Cloud RAN on Kubernetes

Cloud RAN deployments run O-CU and O-DU workloads as containerized network functions on Kubernetes. The swt3-witness Helm chart deploys alongside RAN pods to provide continuous hardware and inference attestation.

Helm Installation for RAN Clusters

# Install with RAN-specific tolerations and ARM node support
# Replace taint keys with your cluster's actual RAN node taints
helm install swt3 oci://ghcr.io/tenova-labs/charts/swt3-witness \
  --set 'tolerations[0].key=ran.example.com/du' \
  --set 'tolerations[0].operator=Exists' \
  --set 'tolerations[0].effect=NoSchedule' \
  --set 'tolerations[1].key=ran.example.com/cu' \
  --set 'tolerations[1].operator=Exists' \
  --set 'tolerations[1].effect=NoSchedule' \
  --set config.interval=300 \
  --set config.clearingLevel=1

RAN Kubernetes clusters typically use node taints to isolate DU and CU workloads from general-purpose pods. The tolerations above ensure the SWT3 DaemonSet runs on RAN-specific nodes alongside the network functions it attests.

Node Affinity for Multi-Silicon RAN

Cloud RAN clusters often mix silicon: ARM Neoverse for O-DU processing, Qualcomm Cloud AI 100 for inference acceleration, and x86 for O-CU and Non-RT RIC. The DaemonSet runs on all architectures and discovers hardware automatically.

# values-ran.yaml (example -- adjust tolerations for your cluster)
tolerations:
  - operator: Exists    # Run on all tainted RAN nodes

nodeSelector: {}        # All nodes (DU, CU, RIC, MEC)

config:
  mode: cloud
  interval: 300         # 5-minute attestation cycle
  clearingLevel: 1
  agentId: ""           # Auto-generates from pod hostname

resources:
  requests:
    cpu: 50m            # Minimal footprint on latency-sensitive nodes
    memory: 32Mi
  limits:
    cpu: 200m
    memory: 128Mi

Resource limits are deliberately low. RAN nodes are latency-sensitive, and the DaemonSet must not compete with network functions for CPU cycles. The 50m CPU request is sufficient for periodic hardware discovery and anchor generation.

For detailed Kubernetes multi-silicon attestation patterns including GKE, EKS, and AKS, see Multi-Silicon Kubernetes Attestation.

5. Latency-Constrained Attestation

The fundamental tension in telecom AI attestation is that compliance evidence must be generated without affecting the real-time performance of network functions. SWT3 resolves this through a two-tier attestation model.

Tier 1: Real-Time Path (Level 0)

For AI decisions in the O-DU and Near-RT RIC control loops, Level 0 captures only three numeric factors and a timestamp. The witness call performs no network I/O and no cryptographic operations in the real-time path. Factors are written to a local buffer with minimal overhead relative to the scheduling interval.

Factor	Encoding	Payload Size
factor_a	Model version hash (first 8 digits)	4 bytes
factor_b	Decision category code	4 bytes
factor_c	Confidence bucket (0-9)	4 bytes
Timestamp	Millisecond epoch	8 bytes
Total		~200 bytes per anchor

These factors are written to a local write-ahead log (WAL) on the node. No network I/O occurs in the real-time path.

Tier 2: Batch Replay (Level 1-2)

A background process reads the WAL and enriches anchors with additional context: model metadata, operational parameters, and (at Level 2) subscriber context hashes. Enriched anchors are flushed to the clearing house on a configurable interval (default: 60 seconds for RAN, 5 seconds for Non-RT RIC).

# Configure WAL flush interval for RAN nodes
from swt3_ai import Witness

witness = Witness(
    tenant_id="your-tenant-id",
    api_key="axm_YOUR_KEY",
    agent_id="du-node-arm-01",
    clearing_level=0,
    on_flush=lambda anchors: logger.info(f"Flushed {len(anchors)} RAN anchors"),
    flush_interval=60.0        # 60-second batch flush (seconds)
)

6. Multi-access Edge Computing (MEC)

ETSI Multi-access Edge Computing (MEC) deploys AI applications at the network edge, co-located with or adjacent to base stations. MEC applications run on the MEC platform, which provides APIs for radio network information, location, bandwidth management, and application lifecycle.

MEC Architecture and SWT3 Integration

MEC Component	AI Use Cases	SWT3 Integration Point
MEC Application	Video analytics, AR/VR optimization, autonomous vehicle coordination, local content recommendation	SDK integration in application code. Standard witness.wrap() calls.
MEC Platform	Application placement optimization, resource scheduling, traffic steering	Platform-level DaemonSet. Hardware attestation (AI-HW.1) for edge servers.
MEC Orchestrator	Multi-site placement, lifecycle management, SLA enforcement	Orchestrator plugin. Witnesses placement decisions and SLA compliance.

MEC applications differ from core RAN AI in that they typically have more relaxed latency requirements (10ms-100ms vs sub-1ms) and richer input data (video frames, sensor streams vs radio measurements). This means MEC applications can use Level 1 or Level 2 clearing without performance concern.

NFV AI Compliance

Virtualized Network Functions (VNFs) and Cloud-Native Network Functions (CNFs) that incorporate AI/ML capabilities fall under the same attestation model. The SWT3 DaemonSet running on the NFV infrastructure provides hardware attestation, while the VNF/CNF integrates the SDK for inference witnessing.

Key attestation targets for NFV AI:

• AI-INF.1 for each inference decision made by the VNF
• AI-HW.1 for the underlying NFVI compute hardware
• AI-DRIFT.1 for model performance changes as network conditions evolve
• AI-CHAIN.1 for service function chains where multiple AI-enabled VNFs process traffic sequentially

7. Network Slicing AI Governance

5G network slicing creates isolated virtual networks tailored to specific service types. AI systems manage slice lifecycle, resource allocation, and SLA enforcement. Each slice type has distinct compliance requirements based on the data it carries and the criticality of decisions.

Slice Type	AI Decisions	Data Sensitivity	Recommended Clearing Level
eMBB (Enhanced Mobile Broadband)	Bandwidth allocation, video optimization, content caching	Aggregate traffic patterns	Level 0 (Analytics)
URLLC (Ultra-Reliable Low-Latency)	Resource reservation, latency guarantee enforcement, failover prediction	Mission-critical service metadata	Level 1 (Standard)
mMTC (Massive Machine-Type)	Device clustering, congestion prediction, sleep scheduling	Device identifiers (IoT, not subscriber)	Level 0 (Analytics)
V2X (Vehicle-to-Everything)	Trajectory prediction, collision avoidance coordination, platoon management	Vehicle location, safety-critical decisions	Level 2 (Sensitive)
PPDR (Public Protection and Disaster Relief)	Priority access, emergency capacity allocation, coverage prediction	Emergency responder data, location	Level 2 (Sensitive)

The AI system that orchestrates slice lifecycle (creation, scaling, termination) should be witnessed at Level 1 regardless of slice type, because slice orchestration decisions affect all subscribers on the network.

8. Clearing Level Decision Matrix

The following matrix maps common telecom AI use cases to recommended clearing levels. Use this as a starting point and adjust based on your specific regulatory requirements and data processing agreements.

Use Case	Layer	Level	Rationale
Beamforming optimization	O-DU	0	Physical layer, no subscriber data, sub-1ms constraint
Scheduling optimization	O-DU	0	Physical layer aggregate metrics
Traffic prediction (aggregate)	Near-RT RIC	0	Cell-level aggregates, no individual subscriber data
Interference management	Near-RT RIC	0	Inter-cell coordination, RF measurements only
Load balancing	Near-RT RIC	1	May trigger handovers affecting individual subscribers
QoS optimization (per-user)	Near-RT RIC	2	Subscriber-specific quality decisions, GDPR personal data
Network slicing orchestration	SMO	1	Service-level decisions affecting multiple subscribers
Anomaly detection (security)	Non-RT RIC	1	Network-wide patterns, potential false positive impact
Subscriber experience optimization	Non-RT RIC	2	Per-subscriber profiling, GDPR Article 22 considerations
Location-aware services	MEC	2	Subscriber location is sensitive personal data
Emergency call routing	O-CU	2	Safety-critical, location data, regulatory retention requirements
Capacity planning (predictive)	SMO	0	Aggregate demand forecasting, no subscriber data
Fault prediction	SMO	1	Infrastructure state, potential service impact
Energy optimization	Non-RT RIC	0	Power consumption metrics, no subscriber data
Spectrum sharing (CBRS/LSA)	Non-RT RIC	1	Regulatory spectrum allocation decisions

9. Multi-Vendor RAN Compliance

O-RAN disaggregation means a single operator's network may include RAN components from multiple vendors. Each vendor supplies AI/ML models for their equipment, but the operator (deployer) bears compliance responsibility for the integrated system.

Vendor	RAN Products	AI Functions	Recommended Integration
Ericsson	RAN Compute, Cloud RAN, Ericsson Silicon	AI-RAN (beamforming, spectrum, energy), Network Intelligence	SDK in xApp/rApp. DaemonSet on Cloud RAN K8s.
Nokia	AirScale, MantaRay SON, ReefShark SoC	Self-Organizing Networks, capacity optimization	SDK in SON functions. DaemonSet on AirScale cloud.
Samsung	vRAN, Samsung AI-RAN	Virtualized RAN optimization, multi-RAT management	SDK in vRAN workloads. Container sidecar pattern.
Mavenir	Open RAN (O-RAN native), MAVcore	Cloud-native RAN intelligence, edge AI	SDK in CNFs. Helm chart on Mavenir K8s platform.
Rakuten Symphony	Symworld platform	AI-native network automation	SDK integration via Symworld API layer.

Integration patterns above are architectural recommendations based on each vendor's published platform architecture. SWT3 is vendor-agnostic: the Python SDK and Helm chart work on any Kubernetes-based RAN platform.

Provider vs Deployer Boundary

In a multi-vendor RAN, the equipment vendor is the AI provider (supplies the model, training data, and documentation). The mobile network operator is the AI deployer (operates the model in production, bears responsibility for operational behavior). SWT3 witnesses at the deployer boundary: the operator's infrastructure generates attestation evidence for AI decisions made by vendor-supplied models running on operator-managed infrastructure.

This separation means the operator does not need access to vendor model internals. SWT3 witnesses the observable behavior (inputs, outputs, performance metrics) without requiring model weights or training data. The vendor provides model documentation per EU AI Act Article 13; the operator provides operational evidence per Article 26.

For detailed provider-deployer obligation mapping, see GPAI Code of Practice Mapping (Section 11: Deployer Obligations).

10. Telecom AI Silicon

Telecom infrastructure uses a distinct silicon landscape from cloud data centers. The SWT3 hardware attestation procedure (AI-HW.1) generates a cryptographic anchor for each accelerator type, providing continuous inventory of AI compute resources across the RAN.

Silicon	Vendor	RAN Role	Discovery Method	Status
Custom 5nm AI-RAN ASIC	Ericsson	O-DU acceleration, beamforming, Cloud RAN compute	ericsson-hwid	Planned
Cloud AI 100 / AI 200	Qualcomm	Edge inference acceleration, Near-RT RIC xApps	qaic-util	Planned
Neoverse N2 / V2	ARM	O-DU general-purpose processing, CNF hosting	PCI + /sys/devices	Planned
ReefShark SoC	Nokia	Baseband processing, power-efficient RAN compute	PCI fallback	Planned
A100 / H100 / H200	NVIDIA	Non-RT RIC model training, large-scale inference	nvidia-smi	Live
Xeon Scalable	Intel	Traditional RAN baseband, O-CU, FlexRAN	PCI fallback	Live
Gaudi 2 / Gaudi 3	Intel	AI training workloads at operator data centers	hl-smi	Live

All telecom silicon is detectable today via PCI fallback (vendor ID from /sys/bus/pci/devices), which provides device identification, vendor, and accelerator count. "Planned" native discovery methods add richer metadata (memory topology, interconnect detail, serial numbers) but are not required for compliant attestation. PCI fallback produces valid AI-HW.1 anchors on all hardware.

For detailed multi-silicon Kubernetes attestation including Helm chart configuration and discovery internals, see Multi-Silicon Kubernetes Attestation.

11. Regulatory Mapping

Telecom AI systems operate at the intersection of telecommunications regulation, AI regulation, and data protection law. The following control cards map key regulatory obligations to SWT3 attestation evidence.

12. Standards-to-Procedure Mapping

The following table maps specific clauses from telecom standards to SWT3 procedures. This mapping is the publisher's analysis and should be validated against your organization's specific compliance requirements.

Standard	Clause/Requirement	SWT3 Procedure	Coverage
EU AI Act	Art. 12 -- Automatic logging	AI-INF.1, AI-LOG.1	Full
EU AI Act	Art. 9 -- Risk management	AI-DRIFT.1, AI-DRIFT.2, AI-PERF.1	Full
EU AI Act	Art. 14 -- Human oversight	AI-GRD.1, AI-EMRG.1, AI-SAFE.1	Partial
EU AI Act	Art. 15 -- Accuracy, robustness	AI-PERF.1, AI-ROBUST.1	Partial
EU AI Act	Art. 26 -- Deployer obligations	AI-INF.1, AI-HW.1, AI-DRIFT.1	Full
EU AI Act	Art. 50 -- Transparency	AI-INF.1 with CJT metadata	Full
EECC	Art. 40 -- Network integrity	AI-SAFE.1, AI-ROBUST.1, AI-GRD.1	Full
EECC	Art. 40 -- Incident reporting	AI-EMRG.1, AI-INCIDENT.1	Full
3GPP Rel. 18	TR 38.843 -- AI/ML for NR	AI-INF.1, AI-MDL.5, AI-DRIFT.1	Partial
3GPP Rel. 18	TS 38.331 -- RRC AI/ML procedures	AI-INF.1, AI-LCM.1	Partial
O-RAN Alliance	WG2 -- Non-RT RIC A1 policies	AI-INF.1, AI-GRD.1	Full
O-RAN Alliance	WG3 -- Near-RT RIC xApps	AI-INF.1, AI-CHAIN.1	Full
O-RAN Alliance	WG1 -- O-RAN architecture	AI-HW.1 per component	Full
ETSI GS MEC 003	MEC framework and reference	AI-INF.1, AI-HW.1	Full
ETSI GS MEC 011	MEC platform application enablement	AI-INF.1, AI-ACC.1	Partial
ETSI SAI	Securing AI -- threat landscape	AI-ROBUST.1, AI-CYBER.1	Partial
ETSI EN 303 645	Consumer IoT security baseline	AI-HW.1, AI-INF.1	Full
GDPR	Art. 22 -- Automated decisions	AI-INF.1 with CJT fields	Partial
GDPR	Art. 35 -- DPIA	AI-DPIA.1, AI-IMPACT.1	Full
ePrivacy	Art. 5 -- Confidentiality of comms	Clearing Level 2+ for subscriber data	Full

Full = SWT3 generates primary evidence for this obligation. Partial = SWT3 provides supporting evidence; additional organizational measures required.

13. Sample SWT3 Witness Anchors

The following examples show what SWT3 anchors look like for common telecom AI scenarios.

Beamforming Inference (O-DU, Level 0)

{
  "procedure": "AI-INF.1",
  "anchor": "SWT3-E-TELCO-AI-INF1-PASS-1786537200-a3f7c1209e44",
  "fingerprint": "a3f7c1209e44",
  "clearing_level": 0,
  "factor_a": 30100007,
  "factor_b": 3,
  "factor_c": 8,
  "model_id": "beam-predictor-v3",
  "agent_id": "du-node-arm-site-0142",
  "provider": "on-device",
  "metadata": {
    "ran_function": "beamforming",
    "control_loop": "o-du"
  }
}

Model Drift Detection (Near-RT RIC, Level 1)

{
  "procedure": "AI-DRIFT.1",
  "anchor": "SWT3-E-TELCO-AI-DRIFT1-PASS-1786540800-b2e6d0318f57",
  "fingerprint": "b2e6d0318f57",
  "clearing_level": 1,
  "factor_a": 20100003,
  "factor_b": 94,
  "factor_c": 2,
  "model_id": "spectrum-optimizer-v2",
  "agent_id": "near-rt-ric-xapp-drift-01",
  "drift_metric": "mse",
  "drift_value": 0.0023,
  "drift_threshold": 0.005,
  "drift_status": "within_bounds"
}

Hardware Attestation (RAN DU Node, Level 1)

{
  "procedure": "AI-HW.1",
  "anchor": "SWT3-E-TELCO-AI-HW1-PASS-1786544400-c1d5e9427a63",
  "fingerprint": "c1d5e9427a63",
  "clearing_level": 1,
  "factor_a": 4,
  "factor_b": 1,
  "factor_c": 1,
  "silicon_vendor": "arm-neoverse",
  "discovery_method": "pci-fallback",
  "accelerator_count": 4,
  "hostname_hash": "sha256:7f8e9a...",
  "agent_id": "ran-witness-du-node-0142"
}

Emergency Override (O-CU, Level 2)

{
  "procedure": "AI-EMRG.1",
  "anchor": "SWT3-E-TELCO-AI-EMRG1-PASS-1786548000-d0c4f8536b72",
  "fingerprint": "d0c4f8536b72",
  "clearing_level": 2,
  "factor_a": 1,
  "factor_b": 5,
  "factor_c": 1,
  "model_id": "traffic-steering-v4",
  "agent_id": "o-cu-emergency-handler",
  "override_reason": "manual_operator_intervention",
  "override_scope": "sector_alpha_cell_0142",
  "jurisdiction": "EU",
  "legal_basis": "legitimate_interest",
  "purpose_class": "emergency_services"
}

14. Next Steps and Related Guides

This guide covers the telecom-specific aspects of AI attestation. For deeper treatment of individual topics, see the following companion guides:

• Multi-Silicon Kubernetes Attestation -- Helm chart deployment, silicon discovery internals, GKE/EKS/AKS patterns, and telecom RAN cluster configuration
• Mobile Edge AI Attestation -- Device-level edge attestation for Android, iOS, and embedded devices with write-ahead log patterns
• GPAI Code of Practice Mapping -- EU AI Act GPAI transparency obligations, model documentation, deployer obligations (Art. 26)
• Disaggregated Inference Witnessing -- Multi-phase AI with KV cache custody chains, relevant for distributed RAN inference
• Edge AI Attestation -- Air-gapped and connectivity-constrained environments, relevant for remote cell sites
• Cross-Silicon Compliance -- Multi-vendor silicon landscape and drift detection across hardware platforms
• AI Factory Compliance Blueprint -- GPU infrastructure attestation for Non-RT RIC training workloads

For SDK documentation, code examples, and API reference, visit the SDK Documentation portal. To verify any SWT3 witness anchor, use the public verifier.