Enabling safe, scalable AI agent adoption in financial services

asilon.ai — the Agentic AI Risk & Enablement Platform

Prepared for Calamity Capital Presented by Simon Iliadis, Sales Engineer Wednesday, June 17, 2026

Agenda

What we'll cover today

01
Your challenges with agentic AI
Where AI agents are outpacing governance
02
The risk of unmanaged agents
Business, regulatory and reputational exposure
03
Introducing asilon.ai
Your agentic AI control plane
04
Capabilities and live demo
How the platform works, end to end
05
Tailored value for financial services
A concrete journey for your operations
06
Proposed POV, criteria and next steps
A focused path to value in your environment
Product overview & demo ~20 min POV discussion ~10 min
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The problem

Your current challenges with agentic AI

Explosive agent sprawl
150,000+1

AI agents in an average Fortune 500 enterprise by 2028 — up from fewer than 15 in 2025. Customer service, fraud, KYC/AML and copilots become "shadow AI" faster than anyone can track.

Mounting regulatory pressure

EU AI Act high-risk systems, DORA and GDPR demand transparency, accountability, human oversight and auditability.

Security & data risk

Agents hold broad permissions into core systems and sensitive data — exposed to prompt injection, unauthorized actions and exfiltration.

Governance gaps

No central inventory, inconsistent ownership, and no reliable way to assess posture or enforce policy at scale.

Operational friction

Manual reviews slow innovation; fear of fines, loss or reputational damage delays valuable AI deployments.

From fragmented to governed

The opportunity is not less AI — it's the same AI, under control, with the evidence to prove it.

1. Gartner, “Gartner Identifies Six Steps to Manage AI Agent Sprawl,” press release, April 2026.

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The stakes

The risk of unmanaged agentic AI

Impact →
Med
High
Critical
Low
Med
High
Low
Low
Med
Likelihood →

Unmanaged agent proliferation moves risk up and to the right — the marked cell is where a high-permission agent on customer data lands today.

Security
$4.9M1
Global average total cost of a data breach — the exposure a single over-permissioned agent can create.
Compliance
7% / €35M2
Maximum EU AI Act penalty exposure for prohibited-practice violations.
Operational
Months
Time-to-value lost as initiatives stall in manual review queues.
Reputational
Trust
One visible AI failure erodes hard-won customer and regulator confidence.

1. IBM, Cost of a Data Breach Report 2024 (global average $4.88M).  2. EU AI Act, Regulation (EU) 2024/1689, Art. 99 — fines up to €35M or 7% of worldwide annual turnover.

Leading institutions are moving from risk avoidance to risk-enabled acceleration.

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The platform

Introducing asilon.ai

The visibility, risk intelligence, governance controls and remediation workflows you need to confidently register, assess, govern and scale AI agents — without creating security, compliance or operational disasters.

Step 01
Register agents
Step 02
Assess & classify risk
Step 03
Score control maturity
Step 04
Route remediation
Step 05
Review & maintain lifecycle

Built around six core pillars

Governance & ownership
Inventory & risk classification
Identity, access & tool control
Data protection & privacy
Audit, monitoring & evidence
Compliance & lifecycle management
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How it works

Platform capabilities and live demo

Agent inventory & discovery

One registry with owner, purpose, permissions, data access, autonomy level and integrations.

Risk assessment & classification

Multi-dimensional scoring across data sensitivity, action scope, behaviour and compliance mapping.

Control maturity scoring

Benchmark governance posture per agent and surface exactly where the gaps are.

Remediation & routing

Prioritised playbooks routed to the right teams — security, compliance, DevOps.

Change review & governance

Pre-deployment reviews, policy enforcement and full audit trails on every change.

Reporting & integrations

Executive and compliance views, plus connections to IAM, SIEM, GRC and AI platforms.

Demo UI example
dash.asilon.ai
Agents
12
10 active
Open risks
7
3 high, 4 med
Coverage
78%
+6 pts

Risk posture

7Total
High 3
Medium 4
Low 0

In a live demo we would show

  1. Onboarding an existing fraud-detection agent with full context
  2. Viewing its risk score and control-maturity gaps
  3. Triggering a remediation workflow to the right team
  4. Generating a compliance-ready evidence report
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For your business

Tailored value for financial services

Scenario: safely scaling a real-time fraud-detection agent with access to customer accounts and core systems — while meeting EU AI Act, DORA and GDPR expectations.
01 Discover
Existing & pilot agents

Inventory what's already running across business units.

02 Assess
Prioritise real risk

Access to core systems and customer data first.

03 Control
Implement guardrails

Right permissions, policies and tool limits.

04 Monitor
Evidence for auditors

Ongoing monitoring and audit-ready evidence.

05 Scale
Production with confidence

Accelerate rollout, backed by proof.

Weeks, not quarters
Faster safe time-to-production for AI initiatives
Less manual effort
Reduced governance overhead through automation
Board-ready
Clear risk visibility for leaders and regulators
Protected ROI
Value realised from AI investments, safely
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The proposal

Proposed POV plan and timeline

Phase
W1
W2
W3
W4
W5
W6
Kickoff & discoveryWorkshop & agent inventory collection
Week 1
Registration & assessmentRisk & maturity scoring, analysis
Weeks 2–3
Remediation planningQuick wins & dashboard reviews
Week 4
Demos & success reviewFeedback & production roadmap
Weeks 5–6

POV objectives

  • Validate platform value on a focused set of agents
  • Demonstrate risk visibility, control and remediation
  • Align on integration and broader rollout path
Recommended starting scope
10–25 agents in one high-impact area — fraud detection or customer operations / claims.
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What good looks like

POV success criteria

Register and risk-assess ≥ 80% of targeted agents, with rich context captured.
Deliver prioritised risk & maturity reports accepted by security and compliance stakeholders.
Identify quick-win remediations and demonstrate the routing and workflow process.
Positive stakeholder feedback on usability, visibility and regulatory relevance.
Clear integration requirements and a defined path to broader rollout.
Documented evidence generation suitable for internal audit or regulatory review.
We will jointly define and refine these criteria in the kickoff, so they directly support your priorities.
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Where we go from here

Next steps and discussion

1
Agree scope, criteria & timeline
2
Schedule technical deep-dive
POV kickoff

What would help us prepare

  • Any existing AI agent inventory or pilot list
  • Current AI, model-risk or governance policies
  • Priority use cases you most want to de-risk
  • Key stakeholders to involve — security, compliance, business
Download the white paper (PDF)Governing the agentic enterprise — full framework
asilon.ai
Thank you

Let's enable safe, scalable AI — together. Questions welcome.

Simon Iliadis, Sales Engineer
simon@asilon.ai
linkedin.com/in/simoniliadis
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