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Sovereign AI · A Neutral Standard

“Sovereign AI without Sovereign Defense is just Sovereign Risk.”

Every country is building sovereign AI.
The detection knowledge that defends it should belong to no single vendor.
That layer has to be neutral.

Countries are investing heavily in sovereign AI they can control and audit. Yet the detection rules that defend those agents still come mostly from private vendors in a single country — the very dependency sovereign AI was meant to remove.

ATR is the neutral infrastructure for that context: MIT-licensed, 836machine-readable detection rules, community-maintained — adoptable, auditable, extensible, or forkable by any country or organization, dependent on no single vendor. Adoption to date includes rule packs merged into Cisco AI Defense's open-source skill-scanner and Gen Digital Sage, ATR example directories merged into Microsoft AGT, plus references from MISP and OWASP — each evidenced by a merged pull request in a public repository.

RULES
836
MIT licensed · count read live from rules/
GARAK RECALL · HUNT
92.3%
enforce lane (for blocking, stable rules only) 0.9% · 650 in-the-wild · ATR 4.1.5, measured 2026-10-15 · partly closed-book: many rules were written from this corpus
FALSE POSITIVE
withdrawn
per-lane FP rates withdrawn · awaiting re-measurement
01 · THE MOMENT

Why Sovereign AI became a national imperative

Many countries now treat AI as a national capability they must control themselves, investing heavily in models built on their own language, data, and compute so that critical systems do not depend on foreign platforms. That investment spans many regions:

India
Tata + Reliance · Hindi / Marathi LLMs
$ multi-billion
Japan
SoftBank + KDDI · Japanese LLMs · disaster resilience AI
$ multi-billion
United Kingdom
NVIDIA AI infrastructure commitment
£1B (≈ $1.3B)
France
Mistral AI + 18,000 Grace Blackwell
$ multi-billion
Saudi / UAE / Korea
2025 US-Saudi Investment Forum and others
$ multi-billion
Taiwan
Foxconn + TSMC + gov AI supercomputer · Constellation HQ
NT$ 40B+

The driver is sovereignty anxiety — no country wants its critical data and intelligence running in US clouds, nor wants to be shut off or censored by foreign platforms at critical moments. The Asia-Pacific sovereign-AI infrastructure market alone is estimated at $9–14B in 2026, projected to reach $23–47B by 2030.

“Every country needs to own the production of their own intelligence. The first thing I would do is codify the language, the data of your culture into your own large language model.”Jensen Huang · World Governments Summit · Dubai 2024
02 · THE NEUTRALITY PROBLEM

Owning the model is one thing. Owning the detection knowledge that defends it is another.

These countries have their own models and their own compute. But when those AI systems become agents — autonomous actors, tool users, MCP clients, transaction executors — no widely accepted agent security detection standard exists.

The regulators know it. The EU AI Act was negotiated before agentic AI arrived — its risk tiers assume systems that assista human decision, not systems that decide and act on their own. NIST launched its AI Agent Standards Initiative in February 2026. The frameworks are catching up; in ATR’s view, what they still need underneath them is an executable detection layer.

Most available detection rule sets are closed-source and concentrated in private vendors within a few countries. In a 2026 survey, 94% of IT leaders said they fear AI vendor lock-in — and for detection knowledge the lock-in is sharper: a country can control its model yet still defend it through proprietary, unauditable rule sets it cannot inspect. Defense knowledge that cannot be opened, inspected, or forked is incomplete sovereignty.

“While these systems promise significant benefits for productivity and innovation, they present unique security challenges.”NIST · News release, 2026-01-12: CAISI's RFI on securing AI agent systems
CONCERNED
87%
but only 11% report being prepared · CSA 2026
OPEN STANDARDS
0
vendor-neutral open standards for the agent detection layer, before ATR
MYTHOS
83.1%
automated vuln discovery · attack capability evolving faster than signature detection
03 · WHY A NEUTRAL STANDARD

Why this context calls for an open standard, not another product

MIT License · 836 detection rules · protocol-agnostic. Derived from real attack corpora, classified to NVIDIA garak / OWASP Agentic / MITRE ATLAS taxonomies. Adoptable by any country, contributable by any organization. No geopolitical risk. No vendor lock-in.

Almost every ATR rule is a set of regular expressions matched against what an agent reads and does: prompts, tool calls, skill files, MCP traffic. A rule catches the phrasings its patterns cover, not the intent behind them, so it can be evaded: a paraphrased, translated or encoded payload can slip past it. Fewer than half of the rules carry evasion tests, which record known bypass attempts and whether each gets through, and LIMITATIONS.md sets out what regex cannot detect. Someone who finds a bypass can submit it as a probe, and a maintainer can turn it into a new rule, or extend an existing one to cover it, under a stable ID.

03B · INTEROPERABLE WITH EXISTING TOOLING

No need to replace your existing SOC tooling

Every bank, hospital, and semiconductor fab’s security operations center already runs on an existing detection stack — Splunk, Elastic, one SIEM or another. A neutral standard nobody can use without ripping those out is a standard nobody adopts.

So ATR rules are designed to be exportable: the same machine-readable rule can be emitted as Splunk SPL, an Elastic query, or generic regex, and fed straight into an existing detection pipeline. The standard lives in the rule itself, bound to no single vendor’s execution engine.

The converter is part of ATR’s open reference implementation (MIT). The attack surface moved — SQL injection into reasoning chains, command injection into tool calls, SSRF into MCP connections — but existing detection infrastructure does not have to reset to zero to keep up.

# Open reference CLI (MIT) — export ATR rules into existing tooling
atr convert splunk --output ./atr-rules.spl
04 · ECOSYSTEM

Already adopted across major security stacks

Cisco AI Defense
Merged
PR #99 merged (2026-04-22) · ATR rule pack in the open-source repo cisco-ai-defense/skill-scanner
Microsoft
PR Merged
agent-governance-toolkit · PR #2308 + #3025 · ATR import and ACS annotator examples (earlier rule-pack YAML no longer in the repo)
Gen Digital Sage
Merged
PR #33 merged (2026-05-11) · Full ATR rule pack in the Sage risk-scoring layer under the Norton/Avast/LifeLock parent

0 → 836 rules · three merged upstream integrations (Cisco's open-source skill-scanner, Gen Digital Sage, Microsoft AGT example directories) · peer-standard references · full adopter list in ADOPTERS.md · MIT licensed · DOI 10.5281/zenodo.19178002

05 · COMMUNITY-GOVERNED

An open standard, governed by its contributors

ATR is not a single lab’s or vendor’s deliverable. Every rule passes through a public, auditable pipeline: pull request → automated safety gate → community review → merge → npm publish. Governance rules are codified in GOVERNANCE.md; every rule carries provenance metadata; any contributor can challenge, correct, or extend the taxonomy.

There are three levels of participation in the standard:

  • →Contribute rules: translate AI agent attack samples you've observed into ATR YAML rule PRs
  • →Contribute probes: define new attack classes so rule generation can be systematically mapped
  • →Contribute validation: test FP/Recall against real attack corpora, surface detection gaps

For sovereign AI, open governance is itself a structural requirement — attacks are not defined by a single lab, and defense should not be defined by a single vendor.

06 · HOW ANY COUNTRY ADOPTS IT

How any country adopts a public-good standard

No country owns this standard; any country can adopt it— the same way one adopts Linux, OpenSSL, or Sigma: download it, audit it, run it in your own environment, contribute the rules you’re missing back upstream. Because it is an MIT public good, adoption needs no one’s permission and carries no geopolitical strings.

This is also why the international picture matters. The AI Safety Institutes network — launched in San Francisco in 2024 with ten founding members and now coordinating across jurisdictions — is working toward safety evaluations that are comparable from one country to the next. Comparability needs a shared, machine-readable detection format. A neutral standard is what keeps one country’s findings legible to another’s — the way a CVE filed in Tokyo means the same thing in Berlin.

PROPERTY 01
Auditable
Every rule is public YAML with provenance metadata. Adopters can inspect the detection logic rule by rule, with no black box to trust.
PROPERTY 02
Localizable
Rules are forkable and extensible. Any country can add attack samples it observes in its own environment, without depending on an external vendor's release cadence.
PROPERTY 03
Upstream-contributable
Improvements flow back via public pull requests through an automated safety gate and community review. A gap one country fixes benefits every adopter.
PROPERTY 04
Stable identifiers
Rules use CVE-style IDs (ATR-YYYY-NNNNN) that never change after publication — safe for government documents, audit reports, and CI scripts to cite long-term.

Adoption is not a proposal — it is three commands. No license desk, no procurement cycle, no one's permission required:

# Adopt the open standard — MIT, no fee, no geopolitical strings
npm install -g agent-threat-rules
atr scan ./agent-config.json     # audit your own agents against 836 rules
atr convert splunk               # or export into the SOC you already run
07 · OPEN CALL

Who can take part

ATR is an MIT-licensed open standard — non-exclusive, owned by no single vendor. If you believe the sovereign-AI context needs a vendor-neutral, auditable detection standard, any of the following can take part directly:

  • Governments · public sector · national cybersecurity agenciesCan evaluate, audit, or localize ATR as a neutral reference standard for AI agent security, or contribute anonymized attack samples upstream.
  • Enterprise security teams · CISOsFinance, telecom, healthcare, critical infrastructure — integrate the open rules into their agent runtime and contribute detection gaps they hit back to the standard.
  • Research institutions · academic labsGroups working on AI agent attacks, red-teaming, or threat intelligence who want to use ATR as a benchmark or contribute rules.
  • Open-source communities · standards bodiesOpenSSF, OWASP, MITRE, CSA, Linux Foundation, ROOST — any partner willing to help integrate ATR into the existing security standards ecosystem.
  • Security vendors · tool buildersTeams willing to integrate ATR rules into existing products (SIEM, SOC, agent runtime, MCP gateways).
  • Journalists · analysts · policy researchersIf you're reporting on or researching sovereign AI, agent security, or global AI governance, the standard's rules, data, and methodology are open to citation, scrutiny, and challenge.
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Pass this on

A standard works by being seen, scrutinized, and adopted widely. If this framing is useful to your peers, pass it along.

Adam Lin
ATR Initiator
MIT License · Open Standard
agentthreatrule.org
836 Rules · NIST AI RMF mappings · Spec 3.5.0 (Working Draft)