AEGIS operating model (Ariana.Digital) is the Agentic Enterprise Governance & Intelligence Standard: the operating system for governed agentic AI in regulated enterprises. It turns inventory, controls, oversight, and cost discipline into the architecture that lets agents reach production.
Unified Enterprise AI Governance · 2026
The operating system for governed agentic AI
Each pillar addresses a distinct governance domain: accountability, inventory, assessment, agentic controls, transparency, monitoring, and evolution, interconnected through a shared evidence layer and common risk language. Two cross-cutting layers thread through the stack: cost governance and supply-chain & access resilience. One architecture satisfies cross-regulatory and G7 2026 voluntary-signal expectations simultaneously, so governance accelerates production instead of delaying it. Configure the vendor control plane you already own. Do not bolt on a fifth stack.
Entra Agent ID, registry, and policy templates. Agentforce session tracing can feed this plane. AEGIS Pillars 1, 2, and 4.
Discover, govern, and measure agents across 30+ systems. AI Workflow Factory (6 Oct 2026) runs the loop under the same tower. AEGIS Pillars 1, 2, and 6.
Agent Script, Guardrails, and Data 360 session tracing. Named agents that persist for weeks need standing entitlement review, not session-as-oversight. AEGIS Pillars 2, 4, and 6.
Explore a governed Agentic AI reference architecture across Healthcare and Financial Services: 875 operating capabilities, 1,072 six-layer workflows across L1–L3, AEGIS controls, and source-linked industry examples you can use to shape a defensible transformation scope.
Each deadline maps to AEGIS pillars and governance outputs, not one-off projects. See what passing looks like before examiners or regulators ask.
Pass: inventory of GenAI/agentic device functions, deployer-owned logs, human checkpoints, and a stop path the institution controls. Discussion paper, not a final rule.
See AEGIS mapping for US FDA GenAI devicesPass: named owners, resources, and timelines for patching, monitoring, third-party risk, and recovery under DORA. Letter SSM-2026-0301. US SI subsidiaries are in scope.
See AEGIS mapping for ECB AI-cyber plansPass: disclose AI interactions, label synthetic / deepfake content, traceable system documentation. Chatbot and deepfake duties were not delayed by the Omnibus.
See AEGIS mapping for EU AI Act Article 50Pass: named halt owner, reversibility rung, logging you own, tested stop path. CSBS (16 Sep 2026) is a discretionary examiner tool. SR 26-2 left gen/agentic AI out of scope on 17 Apr 2026.
See AEGIS mapping for CSBS overlay and SR 26-2Pass: machine-readable marking for GenAI placed on the market before 2 Aug 2026; Omnibus Art. 5 additions (NCIM/CSAM) take effect the same day.
See AEGIS mapping for Art. 50(2) marking and Art. 5Pass: point-of-interaction notice, post-adverse-outcome disclosure, recordkeeping, and a path to human review for covered ADMT. Federal court stayed SB 24-205 AG enforcement (Apr 27, 2026); SB 26-189 signed May 14. AG has said he will not enforce until rulemaking concludes — confirm posture with counsel.
See AEGIS mapping for Colorado AI SB 26-189Pass: pre-use notice, opt-out for significant decisions (employment, credit, housing, health, education), access to ADMT logic + appeal rights.
See AEGIS mapping for California CCPA ADMTPass: FRIA, risk mgmt system, human oversight, logging, conformity assessment, post-market monitoring. Digital Omnibus (Reg. (EU) 2026/1744) in force 27 Jul 2026. Annex III 2 Dec 2027; Annex I embedded products 2 Aug 2028. EU Machinery Regulation applies 1 Jan 2027 to AI in machinery.
See AEGIS mapping for EU AI Act high-riskThe seven AEGIS control domains stay constant. The engagement tier changes with the work your program needs now.
A structured governance assessment across inventory, accountability, controls, evidence, regulatory deadlines, and agentic risk. The output is a board-ready finding set and ranked remediation roadmap.
Best for: proving where the program stands before policy or tooling spend.
Scope the diagnostic →Implement the controls, policies, approval paths, evidence trails, and tooling identified by the diagnostic. The work ends in operating artifacts, not a best-practice deck.
Best for: moving from governance intent to controls that engineering and second line can test.
Plan the build →Ongoing managed governance and monitoring: regulatory tracking, control health, evidence maintenance, incident support, cost oversight, and executive reporting.
Best for: keeping controls current after the audit or launch that validated them.
Design the run model →Each card specifies the exact governance activities, the regulations it satisfies, agentic AI-specific controls, and the roles accountable.
Build the organizational structure that makes AI governance real and defensible
You cannot govern what you cannot see - complete visibility is non-negotiable
Gate deployment on evidence of safety - not assumptions
The technical implementation of governance - where policy becomes code
Legal obligations to disclose, explain, and empower - meeting the person affected by AI
Governance doesn't end at deployment - it intensifies. Every system needs a heartbeat.
AI regulation is evolving faster than any other tech domain - the framework must evolve with it or become a liability rather than a shield
Implement AEGIS once. Each pillar carries compliance weight across multiple regulations simultaneously.
Regulation → pillar coverage
| Regulation | P1 Govern |
P2 Inventory |
P3 Assess |
P4 Controls |
P5 Transpar. |
P6 Monitor |
P7 Evolve |
|---|---|---|---|---|---|---|---|
| EU regulations | |||||||
| EU AI Act - Prohibited Practices (Art. 5) | ● | ● | ◐ | ● | ◐ | ◐ | - |
| EU AI Act - High-Risk AI (Art. 9-17) | ● | ● | ● | ● | ● | ● | ◐ |
| EU AI Act - GPAI Model Obligations | ◐ | ● | ● | ◐ | ● | ● | - |
| EU AI Act - Transparency (Art. 50) | ◐ | ◐ | - | ◐ | ● | ● | ◐ |
| DORA / ECB SSM-2026-0301 AI-enabled cyber | ● | ◐ | ◐ | ● | - | ● | ● |
| GDPR - Automated Decision-Making (Art. 22) | ◐ | ◐ | ● | ◐ | ● | ◐ | - |
| US federal | |||||||
| NIST AI RMF 1.0 (GOVERN / MAP / MEASURE / MANAGE) | ● | ● | ● | ● | ◐ | ● | ● |
| ISO/IEC 42001 AI Management System | ● | ● | ● | ● | ◐ | ● | ● |
| OMB M-25-22 Federal AI Procurement | ● | ◐ | ◐ | ● | - | ◐ | - |
| FTC Section 5 - AI Deception & Unfairness | ● | - | ◐ | ◐ | ● | ◐ | - |
| SEC AI Risk Disclosure (10-K / 8-K) | ● | ◐ | ◐ | - | ● | ● | - |
| SR 26-2 / OCC 2026-13 (traditional models) | ● | ● | ● | ● | - | ● | ◐ |
| CSBS AI Supervisory Framework (gen/agentic overlay) | ● | ● | ◐ | ● | ◐ | ● | ● |
| CRI Financial Services AI RMF | ● | ● | ● | ● | ◐ | ● | ◐ |
| FDA GenAI devices (FDA-2026-N-7874) | ● | ● | ● | ● | ◐ | ● | ● |
| US state laws | |||||||
| Colorado AI Act (SB 24-205 / SB 26-189) | ● | ● | ● | ◐ | ● | ● | ● |
| California CPPA ADMT Regulations | ◐ | ● | ● | ◐ | ● | ◐ | ● |
| California AI Transparency Act (SB 942) | ◐ | - | - | ◐ | ● | ◐ | - |
| Illinois AI in Employment (AIVII) | ◐ | ● | ● | - | ● | ◐ | - |
| NYC Local Law 144 (Hiring Bias Audits) | - | ◐ | ● | - | ● | ◐ | - |
| Texas TRAIGA (Prohibited AI Practices) | ● | ◐ | - | ● | - | - | - |
| G7 France 2026 · voluntary signals | |||||||
| HAIP Reporting Framework 2.0 (OECD, May 2026) | ● | ● | ● | ◐ | ● | ● | ● |
| G7 Évian · Safer digital space for minors | ◐ | ◐ | ● | ● | ● | ◐ | ● |
| G7 Évian · Frontier AI (finance & cyber) | ◐ | ◐ | ● | ◐ | - | ● | ● |
| Model access & jurisdiction resilience (operational) | ● | ● | ◐ | ◐ | - | ◐ | ● |
Autonomous agents that perceive, reason, decide, and act across multi-step workflows introduce governance dimensions traditional ML does not address. These controls layer onto all seven AEGIS pillars.
Classify every agent by how hard its actions are to undo, then set controls to the rung. A draft a person signs is lower risk even when the model is weak. A payment that settles in seconds is higher risk even when the model is strong. This is an Ariana Digital practitioner model, not a standard. It maps to CSBS examiner checks: action limits, human checkpoints, logging, reversibility, halt.
| Rung | What the agent may do | Minimum control | Pillars |
|---|---|---|---|
| 0 · Advise only | Recommend. A person decides. | Disclosure that it is AI. Log the advice. | P5, P6 |
| 1 · Draft, human signs | Write; a named person signs. | Sign-off record. No write without the signature. | P4, P6 |
| 2 · Act with approval | Execute after a prior approval gate. | Approval, reason codes, log. | P4, P6 |
| 3 · Act, reversible | Act inside an undo window. | Named owner, undo path, halt in minutes. | P2, P4, P6 |
| 4 · Irreversible or physical | Settled payment, robot motion, clinical act. | Independent safety or settlement control, tested halt drill, accountable executive. | P1, P4, P6 |
Fixed-fee packages that turn G7 2026 voluntary signals into evidence you can run, disclose, and defend, add in the engagement planner.
Gap review for youth-facing conversational AI: default safety settings, age assurance, parental controls, restricted interaction modes, and Évian-aligned evaluation evidence.
Add G11 in planner →C2PA provenance, synthetic labeling, deepfake detection integration, and takedown workflows, satisfies EU Art. 50 and G7 provenance expectations. Existing governance SKU.
Add G09 in planner →Live exercise for Legal, Risk, Treasury, and Engineering on frontier-model systemic risk, vendor access loss, and cyber misuse, aligned to G7 Évian finance and cyber tasking.
Add G12 in planner →Ungoverned AI spend is a board-level risk hiding in plain sight. Cost governance threads through Pillars 1, 2, 4, and 6 - making compliance the financially optimized choice.
G7 Évian elevated frontier-model access and vendor jurisdiction as operational continuity risks, not compliance checkboxes. This layer threads through Pillars 1, 2, 4, and 7 alongside cost governance.
Microsoft
Adobe
NVIDIAInception
OpenAI
Anthropic
Fixed-fee offerings mapped to AEGIS pillars, G7 2026 signals, and binding regimes (AI Act, Colorado, TRAIGA, ISO 42001, SR 26-2, CSBS). Sized to fit one budget cycle. Outputs you can hand to the board or a regulator.
Before you spend another dollar. Fixed-fee, 2 - 4 week engagements. Most clients start here.
Scoped, outcome-linked engagements. Complexity slider adjusts where in the range you land.
Pick one tier. Monthly retainer, 6 - 12 month minimum.
Fixed package, 4 - 8 weeks to launch. Priced for speed.
Hourly billing rewards slowness. We agree on scope and price upfront, then deliver.
The range you see is the range we quote. Where you land depends on scope, complexity, and data quality.
Month-to-month after the minimum term. If we stop delivering value, you should be able to stop paying.
Full transfer on Build. Your team pairs with ours. Nothing leaves as a black box.
AEGIS operating model (Ariana.Digital) is the Agentic Enterprise Governance & Intelligence Standard: the operating system for governed agentic AI in regulated enterprises, seven pillars, a shared evidence layer, and agentic controls that survive examiner review. It is distinct from Forrester's AEGIS security control baseline; Forrester lists controls, the AEGIS operating model is how the enterprise runs them. See the Forrester AEGIS → AEGIS Model crosswalk.
A US bank with EU customers, EU booking, or EU-placed models still owes AI Act documentation, logging, human oversight, and post-market monitoring on those systems. Those artifacts should compose with the SR 26-2 / OCC 2026-13 model-risk file for in-scope models, and with the CSBS AI exam overlay (action limits, human checkpoints, logging, reversibility, halt) for generative and agentic systems, rather than sit in a parallel program.
A named executive owner, usually the CRO or CISO with a model-risk or operational-risk second line, plus a system inventory owner in technology. Committees review. They do not replace a single accountable owner.
SR 26-2 / OCC 2026-13 replaced SR 11-7 on 17 April 2026 and left generative and agentic AI out of scope. The AEGIS operating model (Ariana.Digital) still treats traditional models as a model of record. For agents it adds the CSBS overlay: named halt owner, reversibility rung, logging the enterprise owns, and a tested stop path.
If the output can change diagnosis, treatment, triage, or medication decisions, treat it as clinical decision support with provenance, drift monitoring, and clinician fallback. Scheduling, coding, and prior-authorization packets can start as administrative copilots with PHI segmentation and SOP fallbacks.
Choose AEGIS Diagnostic for a board-ready assessment, AEGIS Build to implement controls and evidence, or AEGIS Run for managed governance and monitoring. Use the engagement planner to scope the right tier.
Fixed fee, not hourly. Ranges in the engagement planner are the same ones we quote in contracts, finalized after a scoping call. Full IP transfer on Build. Run is month-to-month after the minimum term. Diagnose fees credit toward Build within 90 days.