Cryptographic audit trail for edge AI inference, local model execution, and autonomous agent tool calls on ARM-powered AI laptops. One SDK. No cloud dependency.
Who this is for: CISOs, enterprise security architects, AI governance teams, and developers deploying local AI agents on NVIDIA N1X / ARM-powered laptops with Microsoft Scout, Windows Copilot, or custom MCP-based agent frameworks. Applicable to any organization where AI models run on endpoint hardware rather than cloud infrastructure.
Context: NVIDIA's N1X system-on-chip brings data-center-grade Blackwell GPU architecture to ARM laptops with up to 128GB unified memory. Microsoft's Scout platform enables autonomous AI agents to execute locally with file system access, code execution, and MCP tool calls. This creates a compliance blind spot: enterprise security teams cannot see what local AI agents are doing because no cloud traffic is generated.
When AI inference moves from cloud APIs to local hardware, the entire enterprise security model breaks. Cloud-based AI governance relies on API logs, network monitoring, and centralized audit trails. Local AI agents produce none of these signals.
An autonomous agent running on an N1X-powered laptop with Microsoft Scout can:
Standard cloud security firewalls and API monitoring see zero traffic from these local agent operations. The enterprise is blind to what its local AI agents are doing.
The SWT3 SDK solves this by running alongside the local agent, generating cryptographic witness anchors for every inference, tool call, and agent action. The SDK runs locally on the same hardware. Raw data never leaves the device. Only SHA-256 hashes are transmitted to the compliance ledger.
Each local AI agent security concern maps to SWT3 procedures that produce cryptographic evidence anchors on the endpoint.
| Local AI Threat | SWT3 Procedure | What It Witnesses | Why It Matters |
|---|---|---|---|
| Unmonitored tool calls | AI-TOOL.1 | Every tool/function call with input hash and result | Local file reads, code modifications, and API calls become auditable |
| Unknown agent identity | AI-ID.1 | Agent identity assertion bound to all anchors | Proves which agent performed which action on which device |
| Hardware integrity unknown | AI-HW.1 | GPU/accelerator inventory and health | Attests N1X Blackwell hardware state at agent startup |
| TPM/Pluton attestation | AI-HW.3 | TPM 2.0 PCR register state | Hardware root of trust proves firmware integrity on N1X silicon |
| Runtime environment drift | AI-ENV.1 | Environment hash, container isolation, runtime type | Detects if local execution environment was tampered with |
| Dependency vulnerabilities | AI-ENV.2 | Dependency manifest with version pins and CVE count | Local Python/Node packages may have known vulnerabilities |
| Local model drift | AI-DRIFT.1 | Drift metrics against production baseline | Local fine-tuned models may drift without centralized monitoring |
| No cloud audit trail | AI-AUDIT.1 | Tamper-evident audit log integrity | Creates the audit trail that cloud APIs would normally provide |
| Prompt injection on local agent | AI-SEC.1 | Adversarial input detection scan results | Detects indirect prompt injection from local files or websites |
| Input validation bypass | AI-SEC.2 | Input sanitization verification | Ensures local agent inputs are validated before execution |
| Local inference untracked | AI-INF.1 | Prompt/response hash with model identifier | Every local inference produces a verifiable evidence anchor |
| Agent safe state missing | AI-SAFE.1 | Stop/interrupt mechanism with recovery status | Proves kill switch exists for runaway local agents |
| Multi-agent handoffs invisible | AI-CHAIN.1 | Chain handoff with cycle tracking | Local agent-to-agent delegation becomes auditable |
| Multi-agent delegation scope | AI-MULTI.1 | Permission envelope for delegated tasks | Tracks what permissions local agents grant to sub-agents |
| Adversarial testing gaps | AI-REDTEAM.1 | Red team campaign scope and findings | Proves local agent configurations were adversarially tested |
| Content provenance unknown | AI-MARK.1 | AI-generated content marking metadata | Local AI outputs are marked for provenance per Art. 50 |
The threat: A local Microsoft Scout agent executes tool calls against the file system, local databases, and internal APIs. No cloud logging captures these actions. If the agent is compromised via indirect prompt injection, it can silently modify code, exfiltrate data, or plant backdoors.
How SWT3 addresses it: wrapTool() intercepts every tool call and generates a witness anchor with the tool name, input hash, and result hash. agent_id binds every anchor to the specific agent instance. The evidence is cryptographically signed and independently verifiable, even if the local agent is compromised.
AI-TOOL.1 anchors prove every local tool call was witnessed. AI-ID.1 binds each action to a specific agent. Query the ledger by agent_id + time range to reconstruct the complete local agent activity timeline.
The threat: Enterprise cannot verify that local AI inference ran on authorized, unmodified hardware. A compromised device could run a modified model or tampered runtime without detection.
How SWT3 addresses it: witnessHardware() attests the N1X GPU inventory (CUDA cores, memory, topology) at agent startup. witnessTPMAttestation() captures TPM 2.0 / Pluton PCR register state, proving firmware integrity and hardware root of trust. These anchors are minted before the agent executes any inference.
AI-HW.1 anchors prove the N1X hardware was attested at startup. AI-HW.3 anchors prove TPM integrity was verified. Any hardware tampering between attestation events would be detectable via PCR register drift.
The threat: A local agent enters a runaway loop or is compromised by prompt injection from a local file. Without a kill switch, the agent continues executing with full local system access.
How SWT3 addresses it: witnessSafeState() proves that stop/interrupt mechanisms exist and records every safe state transition (manual, threshold, chain break, policy). witnessSecurityScan() detects adversarial inputs before they reach the agent's execution loop. Together they provide both prevention (scan) and response (kill switch) evidence.
AI-SAFE.1 anchors prove kill switch capability is deployed and tested. AI-SEC.1 anchors prove adversarial scanning is active on agent inputs. Combined with AI-TOOL.1, this creates a complete defense-in-depth evidence chain for local agent operations.
from swt3_ai import SWT3Witness
# Initialize on the local device
witness = SWT3Witness(
tenant_id="YOUR_TENANT",
agent_id="scout-agent-laptop-7829",
signing_key=os.environ["SWT3_SIGNING_KEY"],
)
# Witness every local inference
anchor = witness.witness_inference(
model_id="local-llama-3.1-70b",
prompt_hash=sha256(user_prompt),
response_hash=sha256(agent_response),
)
# Witness every tool call (file read, code exec, API call)
result = witness.wrap_tool("read_file", read_file, path="/src/config.py")
# Witness hardware attestation at startup
witness.witness_hardware(gpu_count=1, accelerator="n1x-blackwell-6144")
The SDK runs entirely on the local device. Raw prompts and responses never leave the machine. Only SHA-256 hashes are transmitted to the compliance ledger. Local inference latency impact is under 1ms per witness call.
| Profile | Best For | Key Features | Command |
|---|---|---|---|
defense-govcon | Classified / CUI environments | CL3, hardware attestation required, TPM mandatory, strict trust mesh | swt3 init --profile defense-govcon |
autonomous-systems | Safety-critical local agents | CL2, high-density witnessing (5/1000 tokens), strict safe state | swt3 init --profile autonomous-systems |
telecom-compliance | Telecom field devices | CL2, FCC transparency, 19 procedures | swt3 init --profile telecom-compliance |
healthcare-clinical | Clinical devices with local AI | CL3, HIPAA, FDA, clinician oversight mandatory | swt3 init --profile healthcare-clinical |
Cloud AI generates API logs automatically. Local AI generates nothing. The SWT3 SDK fills this gap by running as a lightweight companion process on the same device as the AI agent.
| Data | Stays on Device | Transmitted |
|---|---|---|
| Raw prompts and responses | Yes (never leaves) | No |
| Model weights and parameters | Yes | No |
| Local file contents | Yes | No |
| SHA-256 hashes of above | Yes (retained) | Yes (12-char fingerprint) |
| Factor values (numeric metadata) | Yes | Yes |
| Witness anchor (fingerprint) | Yes (retained) | Yes (verifiable receipt) |
For environments requiring quantum-resistant signing on local hardware, switch to ML-DSA-65 (FIPS 204) with one config line:
signing_algorithm: ml-dsa-65The SDK handles keygen, signing, and verification locally on the ARM hardware. No external key management service required.
Install: pip install swt3-ai or npm install @tenova/swt3-ai
Also available: Rust (crates.io), C# (NuGet), Ruby (RubyGems), MCP Server (io.tenova/swt3-witness)
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Contact: engineering@tenovaai.com