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Aegis Systems — enterprise AI that can be audited at the speed it is deployed

Practice 01 · Identity Orchestration & Agent Access

Identity orchestration: one access layer for every identity you run.

Large organisations run several identity providers, decades of legacy apps and now AI agents — each wired to the others by hand. Identity orchestration puts one standards-based layer between them, so you can add MFA, migrate providers, survive an outage and govern agents without rewriting a single application.

36connections to run

YOUR APPLICATIONSYOUR IDENTITY SYSTEMSOne accesslayerPolicyFailoverAuditHR systemCustomer portalLegacy appSaaS appMobile appAI agentCloud identityDirectoryLegacy accessMFA serviceCloud IAMSecond provider
Every app wired to every identity system, one custom link at a time.Illustrative: six applications and six identity systems. [Interpretation]

New guide · Identity orchestration

Identity Orchestration, Plainly Explained

One access layer across every identity provider, application and AI agent — what it is, where it pays off and how to start without a big-bang programme.

Why the window is open now

88%
of enterprise AI agent proofs-of-concept never reach broad production

IDC

80%+
value-realization failure rate across 2,400+ AI initiatives

RAND

2027
OSFI E-23 takes effect, with a minimum model inventory standard

1 May

120–150 kW
per Blackwell-class rack — past the point air cooling functions at all

Direct liquid cooling

Frameworks & Control Regimes Supported

OSFI E-23NIST AI RMF 1.0NIST GenAI ProfileISO/IEC 42001SR 26-2EU AI Act Annex IIIITSP.10.033ISO/IEC 27001OSFI B-10 / B-13SOC 2 Type IIOSFI E-23NIST AI RMF 1.0NIST GenAI ProfileISO/IEC 42001SR 26-2EU AI Act Annex IIIITSP.10.033ISO/IEC 27001OSFI B-10 / B-13SOC 2 Type II
OSFI E-23 · Effective 1 May 2027

Roughly 7 months to a defensible AI model inventory.

Guideline E-23 expands scope to all federally regulated financial institutions — insurers and branches included — and covers every model that carries risk, explicitly including AI and ML systems. It sets a minimum model inventory standard.

A populated inventory is the second artifact we hand over. It is also the one most institutions do not yet have.

See what the assessment produces

The Traceability Spine

  1. 1

    Board risk appetite

    Where the obligation originates

  2. 2

    Control identifier

  3. 3

    Model

  4. 4

    Dataset

  5. 5

    Compute partition

    Evidence returns up the same path

One identifier scheme links obligation → control → model → dataset → compute. It is what makes the assessment reusable and the evidence defensible.

For CISOs, CROs & Heads of Platform

Assessment Control Plane Preview

Streaming

6

Weeks to a Populated Inventory

7

Named Deliverable Artifacts

3

Practice Areas, One Control Spine

4

Disciplines, One Accountable Team

Control Trace CoverageRisk appetite → control → model → dataset → compute
94.6%
E-23 Inventory CompletenessAgainst the OSFI minimum model inventory standard
87/100
Untraced AI SystemsIn production with no control identifier bound
14

Illustrative values. Engagement dashboards are provisioned per-client inside your tenancy — Aegis retains no telemetry post-engagement.

Every figure here is derived from the traceability spine, not self-reported. Ask us to demonstrate the trace on one of your own models.

The Four-Pillar Delivery Model

Four disciplines that must agree before AI reaches production.

Individually these are commodity. What makes them a product is the connective tissue — a single identifier scheme linking a regulatory obligation to a control, a control to a model, a model to a dataset, and a dataset to the compute it runs on.

Where This Lands

Five executives contest the AI budget. Each hears a different sentence.

CIO / CTO

“We will tell you exactly how much AI workload your estate can carry before you need capital.”

Proof they ask for: A capacity model they can run themselves

CISO

“Your AI attack surface is not the model. It is the identity and data path around it.”

Proof they ask for: Threat model for one live AI system

VP Identity & Access / IAM Architect

“Stop migrating applications one rewrite at a time. Put one orchestration layer in front of every identity provider you run, and give AI agents an identity too.”

Proof they ask for: Identity estate map and a one-application proof-of-concept plan

CRO / Model Risk

“We will hand you a populated inventory and a control gap register in six weeks.”

Proof they ask for: Inventory schema and crosswalk

CDO

“If you cannot show provenance for the retrieval corpus, you cannot defend the output.”

Proof they ask for: Lineage map for one corpus

CFO / Chief AI Officer

“We will give you a ranked portfolio and, more usefully, a list of what to stop.”

Proof they ask for: Cost-per-outcome baseline

One Identifier, End to End

Obligation → control → model → dataset → compute.

And the evidence returns up the same path. Ask us to demonstrate the trace on one of your own models.

Scope Your Assessment

Which tier does your estate need?

Set your estate breadth, regulatory context and compute footprint. The model returns the matching tier, duration and fixed-fee band.

Enterprise-Wide
One portfolioAll units, single jurisdictionMulti-entity
02 — Compute Footprint

Landing-zone and residency boundary design leads WS1

Financial Services
StandardFinancialHealthGov / Def
OSFI E-23SR 26-2OSFI B-10 / B-13NIST AI RMF 1.0ISO/IEC 42001

Recommended Tier

Enterprise Standard

Enterprise-wide, single jurisdiction

The default offer — everything anchors here

Duration

6 Weeks

Fixed Fee (CAD)

$171,000 – $201,000

$86,000 creditable against Stage 2

Inventory Scale

40–120 AI systems

Populated to the E-23 minimum standard

Crosswalk Frameworks

5 regimes

Pillar Allocation

2 : 2 : 2 : 1

7 specialists · WS1 : WS2 : WS3 : WS4

Control Exposure IndexHigh
Untraced control surface67 / 100

Indicative only. Final scope and fee are fixed in a 45-minute technical qualification call, and a statement of work follows within two business days.

Book the qualification call