"Promoting Advanced Artificial Intelligence Innovation and Security" mapped to SWT3 witness procedures. Signed June 2, 2026. Deadlines: July 2 and August 1, 2026.
Who this is for: Government contractors, critical infrastructure operators, AI deployers in federal supply chains, CISOs, ISSMs, compliance officers, and legal counsel evaluating EO implications for AI systems in production.
Deadlines: Section 2 directives (cyber defense, CISA BODs, AI Cybersecurity Clearinghouse) require agency action by July 2, 2026. Section 3 directives (frontier model framework, workforce expansion) require action by August 1, 2026. Government contractors should expect flow-down requirements in contract modifications beginning immediately.
On June 2, 2026, the White House signed "Promoting Advanced Artificial Intelligence Innovation and Security", the most significant federal AI policy action since EO 14110. The order has four major sections:
| Section | Directive | Agencies | Deadline |
|---|---|---|---|
| Section 2 | Upgrade federal and critical infrastructure cyber defenses with AI-enabled tools | CISA, NSA, Treasury, DoW, CNSS | July 2, 2026 |
| Section 2(d) | Establish AI Cybersecurity Clearinghouse for vulnerability scanning coordination | Treasury + NSA + CISA | July 2, 2026 |
| Section 3 | Create voluntary framework for pre-release access to frontier models | NSA-led consortium | August 1, 2026 |
| Section 4 | Prioritize criminal prosecution of AI-enabled unauthorized access (CFAA) | DOJ | Immediate |
The EO explicitly names critical infrastructure operators as beneficiaries, including rural hospitals, community banks, and local utilities.
This anti-licensing clause has profound implications for every organization deploying AI:
SWT3 produces exactly this evidence: cryptographic witness anchors generated at inference time, independently verifiable, tamper-evident through Merkle rollups, covering 65 procedures across 28 namespaces.
| EO Directive | Deadline | SWT3 Procedures | What SWT3 Witnesses |
|---|---|---|---|
| 2(a) NSS cyber defense prioritization | Jul 2 | AI-CYBER.1, AI-SEC.1 | Cyber posture attestation, adversarial threat detection |
| 2(c) CISA BODs + critical infrastructure tooling | Jul 2 | AI-ENV.1, AI-ENV.2, AI-AUDIT.1 | Runtime environment attestation, dependency manifest, independent audit trail |
| 2(d) AI Cybersecurity Clearinghouse | Jul 2 | AI-SBOM.1, AI-SUPPLY.1, AI-MDL.5 | AI bill of materials, supply chain risk assessment, model weight integrity |
| 2(e) Vulnerability detection funding | Jul 2 | AI-PERF.1, AI-DRIFT.1 | Performance validation, model drift detection |
| EO Directive | Deadline | SWT3 Procedures | What SWT3 Witnesses |
|---|---|---|---|
| 3(a) Classified benchmarking process | Aug 1 | AI-BASE.1, AI-REDTEAM.1 | Behavioral baseline attestation, adversarial test campaign records |
| 3(b) Voluntary pre-release framework | Aug 1 | AI-INF.1, AI-MDL.1, AI-CHAIN.1 | Inference provenance, model lifecycle tracking, chain of custody |
| 3(c) Anti-licensing clause | Immediate | All 65 procedures | Continuous runtime evidence replaces non-existent pre-market gates |
| EO Directive | SWT3 Procedures | What SWT3 Witnesses |
|---|---|---|
| 4 CFAA prosecution of AI-enabled unauthorized access | AI-ACC.1, AI-TOOL.1, AI-ID.1 | Access control decisions with authorization_id, tool call authorization records, agent identity binding |
| 4 Agent authorization documentation | AI-AUDIT.1, AI-CHAIN.1 | Tamper-evident audit trail, chain forensic timeline |
EO Section 2(d) requires: Treasury, NSA, and CISA to form a voluntary clearinghouse that "coordinates and deconflicts scanning for software vulnerabilities, discovers and validates such vulnerabilities, and coordinates and prioritizes remediation and distribution of vulnerability patches."
How SWT3 addresses this: AI-SBOM.1 witnesses the full AI software bill of materials (model, framework, dependencies, adapters). AI-SUPPLY.1 records supply chain risk assessments. AI-MDL.5 hashes model weight files so any modification is detected. Together, these produce the supply chain visibility evidence that clearinghouse participants will need to demonstrate compliance.
What to show the examiner: AI-SBOM.1 anchors with dependency manifests. AI-MDL.5 anchors showing model weight hashes match between deployments. AI-SUPPLY.1 anchors documenting vendor risk assessment. Chain monitor export showing no unauthorized supply chain changes.
EO Section 2(c) requires: CISA to release Binding Operational Directives facilitating "access to cybersecurity tools and services including covered frontier models for agencies, State and local authorities, and operators of critical infrastructure."
How SWT3 addresses this: AI-ENV.1 witnesses the runtime environment (OS, GPU topology, memory allocation) at inference time. AI-ENV.2 attests the dependency manifest. AI-AUDIT.1 maintains an independent, Merkle-rooted audit trail. When CISA BODs specify evidence requirements for critical infrastructure AI deployments, these anchors satisfy the documentation burden.
What to show the examiner: AI-ENV.1 environment snapshots from each deployment. AI-AUDIT.1 daily Merkle rollup roots proving continuous operation. Chain monitor HTML report showing unbroken audit trail.
EO Section 2(a) requires: The Committee on National Security Systems to "prioritize the cyber defense of National Security Systems" within 30 days.
How SWT3 addresses this: AI-CYBER.1 witnesses the cyber framework posture of AI systems (mapping to NIST CSF, MITRE ATT&CK). AI-SEC.1 records adversarial threat detection events. For AI systems operating within or adjacent to National Security Systems, these anchors prove the cyber defense posture was actively maintained.
EO Section 3(a) requires: An NSA-led consortium to develop a classified benchmarking process for evaluating advanced cyber-capabilities of "covered frontier models."
How SWT3 addresses this: AI-BASE.1 witnesses behavioral baseline attestation (expected performance parameters, drift thresholds). AI-REDTEAM.1 records adversarial test campaigns (attack categories, success rates, mitigation status). Organizations that voluntarily participate in the frontier model framework can use these anchors to demonstrate their models were tested and baselined before deployment.
What to show the examiner: AI-BASE.1 anchors establishing the behavioral baseline. AI-REDTEAM.1 anchors documenting red team campaigns with dates, categories, and outcomes. Merkle proof linking baseline to production deployment.
EO Section 3(b) establishes: A voluntary framework where developers provide government access to frontier models up to 30 days before release. Requires "appropriate confidentiality, cybersecurity, insider-risk, and intellectual-property protection."
How SWT3 addresses this: AI-INF.1 witnesses every inference with prompt/response hashes (proving what the model did without exposing the content). AI-MDL.1 tracks model lifecycle transitions. AI-CHAIN.1 maintains chain of custody across the pre-release evaluation period. These anchors provide an independent record that the model's behavior during government evaluation matches its behavior in production.
EO Section 4 directs: The Attorney General to "prioritize enforcement of federal criminal laws against anyone who utilizes AI to illegally access or damage a computer without authorization." This applies to autonomous AI agents that access systems, call tools, or interact with external services.
How SWT3 addresses this: AI-ACC.1 witnesses every access control decision with the authorization_id field, proving the agent was authorized BEFORE acting. AI-TOOL.1 records every tool call with parameters and outcomes. AI-ID.1 binds the agent's cryptographic identity to every action. Together, these create a forensic record that can demonstrate authorized behavior in a CFAA prosecution.
What to show the examiner: AI-ACC.1 anchors with authorization_id proving pre-action authorization. AI-TOOL.1 anchors showing tool call parameters and access scope. AI-ID.1 agent identity binding proving which agent performed which action. Chain forensic timeline from chain monitor showing the complete action sequence.
Why this matters: If an autonomous agent causes harm and criminal charges are brought under CFAA, the question becomes: was the agent authorized to do what it did? A tamper-evident, Merkle-rooted, independently verifiable audit trail is stronger evidence than internal application logs.
How SWT3 addresses this: AI-AUDIT.1 maintains an independent audit trail with daily Merkle rollups. AI-CHAIN.1 tracks the complete chain of agent actions, handoffs, and delegation decisions. The chain monitor exporter produces forensic HTML/JSON reports suitable for legal proceedings.
| Profile | Sector | EO Alignment | Command |
|---|---|---|---|
defense-govcon | DoD/DoW contractors | Section 2(a-b) + CMMC flow-downs | swt3 init --profile defense-govcon |
nist-ai-rmf | Federal civilian agencies | Section 2(c) + CISA BODs | swt3 init --profile nist-ai-rmf |
healthcare-clinical | Rural hospitals (named in EO) | Section 2(c) critical infrastructure | swt3 init --profile healthcare-clinical |
fintech-model-risk | Community banks (named in EO) | Section 2(c-d) + clearinghouse | swt3 init --profile fintech-model-risk |
microsoft-foundry | Foundry agent deployments | Section 3 + agent authorization | swt3 init --profile microsoft-foundry |
owasp-agentic-top10 | Autonomous agent developers | Section 4 CFAA + agent risk | swt3 init --profile owasp-agentic-top10 |
| Auditor/Examiner Question | Where to Look |
|---|---|
| How do you track AI supply chain dependencies? | AI-SBOM.1 anchors + AI-SUPPLY.1 risk assessment records |
| How do you prove your AI agent was authorized to act? | AI-ACC.1 anchors with authorization_id + AI-TOOL.1 tool call records |
| What evidence do you have of continuous runtime monitoring? | AI-AUDIT.1 daily Merkle rollup roots + chain monitor forensic timeline |
| How do you verify model integrity between environments? | AI-MDL.5 weight file hashes compared across deployments |
| What behavioral baseline exists for your frontier model? | AI-BASE.1 baseline attestation + AI-DRIFT.1 drift detection history |
| How do you trace agent actions to authorization decisions? | AI-ID.1 agent identity + AI-CHAIN.1 chain of custody + authorization_id field |
| How do you prove your runtime environment is hardened? | AI-ENV.1 environment snapshot + AI-ENV.2 dependency manifest |
| What adversarial testing have you performed? | AI-REDTEAM.1 campaign records with attack categories and outcomes |
# pip install swt3-ai
# Initialize with a profile matching your sector
# swt3 init --profile defense-govcon # DoD/DoW contractors
# swt3 init --profile nist-ai-rmf # Federal civilian
# swt3 init --profile healthcare-clinical # Rural hospitals
# swt3 init --profile fintech-model-risk # Community banks
from swt3_ai import Witness
witness = Witness(
endpoint="https://sovereign.tenova.io",
api_key="axm_live_...",
tenant_id="YOUR_TENANT",
agent_id="your-agent-name", # AI-ID.1: agent identity
signing_key="your-signing-key", # Non-repudiation
)
# Wrap your AI client -- every inference is now witnessed
client = witness.wrap(openai_client)
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Analyze the report"}],
)
# Anchor minted: AI-INF.1 (inference) + AI-ID.1 (identity)// npm install @tenova/swt3-ai
import { Witness } from "@tenova/swt3-ai";
import OpenAI from "openai";
const witness = new Witness({
endpoint: "https://sovereign.tenova.io",
apiKey: "axm_live_...",
tenantId: "YOUR_TENANT",
agentId: "your-agent-name",
signingKey: "your-signing-key",
});
const client = witness.wrap(new OpenAI()) as OpenAI;
// Every call through the wrapped client generates witness anchors