Global Institute of Artificial Intelligence - Melbourne

Who Governs When AI Acts?

Sat, Oct 17 · 6:00 PM AEDT

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## What’s On?

Speaker: Sandeep Bhalekar, Founder – Global Institute of Artificial Intelligence
Title: Who Governs When AI Acts?
Abstract:
AI is moving beyond answering questions. Agentic AI systems can now plan tasks, use tools, access information, interact with other systems and take actions with increasing levels of autonomy.
But as AI gains the ability to act, a critical governance question emerges: who controls what the AI is allowed to do?
This session explores how governance needs to evolve for the age of AI agents and increasingly autonomous AI systems. We’ll examine practical approaches to defining authority, setting boundaries, maintaining human oversight and controlling risk without removing the value that autonomy provides.

## What You’ll Learn

✅ From AI That Answers to AI That Acts — Understand why agentic and autonomous AI introduces fundamentally different governance challenges.
✅ Defining AI Authority — Explore how organisations can determine what an AI system may access, decide, execute and escalate.
✅ Human Oversight & Intervention — Understand where human approval should remain mandatory and when an AI system should pause or escalate.
✅ Identity, Access & Permissions — Explore why agent identity and permission boundaries become critical when AI can interact with enterprise systems and tools.
✅ Guardrails for Autonomous AI — Learn how organisations can establish operational boundaries, approval checkpoints and controls around agent behaviour.
✅ Monitoring & Accountability — Understand why AI actions need traceability, logging, evaluation and clear ownership.
✅ Governing Multi-Agent Systems — Explore the additional challenges created when multiple specialised AI agents interact and collaborate.

## Who This Is For

C-suite and executive leaders, CIOs, CTOs, CDOs, Chief AI Officers, AI governance and risk professionals, technology leaders, AI architects, developers, product leaders, consultants and practitioners working with AI agents, enterprise AI or autonomous systems.
This session is particularly relevant for organisations moving from Generative AI experimentation toward agentic AI and AI-enabled business processes.

## Format

90-minute live session featuring practical examples, governance scenarios and discussion around controlling increasingly autonomous AI systems, followed by an open Q&A.
The focus is not simply on how to build AI agents. It is on answering a more important enterprise question:

When AI has the authority to act — how do we govern it?

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Can You Break This AI? AI Red Teaming, Testing & Risk in Practice

Thu, Oct 22 · 6:00 PM AEDT

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https://meet.zoho.com.au/hbcq-ufc-btv

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## What’s On?

Speaker: Sandeep Bhalekar, Founder – Global Institute of Artificial Intelligence

Title: Can You Break This AI? – AI Red Teaming, Testing & Risk in Practice

Abstract: As organisations deploy increasingly capable AI systems and AI agents, asking “Does the AI work?” is no longer enough.
The real questions are: How can it fail? Can it be manipulated? What happens under unexpected or adversarial conditions? And can we discover those weaknesses before they create business risk?
This practical session introduces AI red teaming, testing and evaluation, exploring how organisations can deliberately challenge AI systems to uncover vulnerabilities, unexpected behaviours and failure modes before and after deployment. NIST defines AI red teaming as structured testing designed to identify flaws and vulnerabilities in AI systems, including undesirable behaviours and misuse risks.

## What You’ll Learn

✅ AI Red Teaming Explained — Understand what red teaming is and why organisations deliberately challenge their AI systems.
✅ Finding AI Failure Modes — Explore hallucinations, unsafe outputs, bias, unexpected behaviours and control failures.
✅ Prompt Injection & Agent Hijacking — Understand how malicious instructions can manipulate AI applications and agents. NIST's 2026 agent-security work specifically highlights indirect prompt injection and agent hijacking as risks for agents processing external data.
✅ Testing AI Agents — Explore the additional risks when AI can access data, call tools and take actions.
✅ Risk-Based AI Testing — Learn how testing can be prioritised around system purpose, business impact and potential harm.
✅ From Testing to Governance — Turn discovered weaknesses into guardrails, controls, monitoring and continuous improvement.
✅ Pre-Deployment + Continuous Testing — Understand why testing before launch alone is insufficient and why deployed AI also needs monitoring.

## Who This Is For

AI leaders, CIOs, CTOs, AI engineers, developers, cybersecurity professionals, Responsible AI practitioners, risk and compliance teams, AI governance professionals, consultants and anyone involved in building, testing, deploying or governing AI systems.
No specialist red-team experience is required.

## Format

90-minute practical live session featuring AI failure scenarios, red-team thinking, testing approaches and real-world examples, followed by an open Q&A.
The session is built around one simple challenge:

Before your customers, employees or attackers
find the weakness in your AI — can you find it first?

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Responsible AI Beyond the Buzzwords

Tue, Oct 27 · 6:00 PM AEDT

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Turning AI Principles into Practical Business Decisions -

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***

## What’s On?

Speaker: Sandeep Bhalekar, Founder – Global Institute of Artificial Intelligence

Title: Responsible AI Beyond the Buzzwords: Turning AI Principles into Practical Business Decisions

Abstract: Responsible AI is often discussed through principles such as fairness, transparency, accountability, privacy and human oversight. The real challenge for organisations is turning these principles into practical decisions, processes and controls.
This session moves beyond Responsible AI theory to explore what responsible AI adoption actually looks like when organisations are designing, deploying and operating AI systems.
Participants will explore practical approaches for identifying AI risks, establishing accountability, addressing bias, implementing human oversight and building trust into the AI lifecycle.

## What You’ll Learn

✅ Responsible AI in Practice — Move beyond principles and understand what Responsible AI means operationally.
✅ Fairness & Bias — Identify where bias can emerge and how organisations can manage it.
✅ Transparency & Explainability — Understand when and how AI decisions should be explainable.
✅ Human Oversight — Determine where human review, intervention and approval should remain part of AI-enabled decisions.
✅ Privacy & Data Responsibility — Understand responsible handling of data throughout the AI lifecycle.
✅ Accountability & Ownership — Establish who is responsible for AI systems, decisions and outcomes.
✅ From Principles to Controls — Translate Responsible AI principles into practical governance processes and organisational controls.

## Who This Is For

C-suite and executive leaders, CIOs, CTOs, CDOs, Chief AI Officers, AI governance professionals, Responsible AI practitioners, risk and compliance teams, technology leaders, consultants and professionals involved in building, deploying or governing AI systems.
No specialist Responsible AI background is required.

## Format

90-minute live session combining practical Responsible AI scenarios, organisational examples and actionable approaches, followed by an open Q&A.
The session is built around one important question:

Responsible AI sounds good in principle — but what does it actually look like in practice?

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HUMAN-IN-LOOP ISN’T ENOUGH | Turn Principles into Controls You Can Actually Use

Thu, Oct 29 · 6:00 PM AEDT

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Online

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Event Confirmation Form!
➡️ We kindly request that all attendees complete the event confirmation form to confirm your attendance:
https://meet.zoho.com.au/pxmi-wxn-ovz

***

## What’s On?

Speaker: Sandeep Bhalekar, Founder – Global Institute of Artificial Intelligence

Title: Human-in-Loop Isn’t Enough: Turn Principles into Controls You Can Actually Use

Abstract: Adding a human approval step to an AI workflow does not automatically make the system responsible, safe or well-governed.
Effective human oversight requires organisations to clearly define who has authority, when intervention is required, what decisions can be overridden, how exceptions are escalated, and how actions are recorded and reviewed.
This session explores how organisations can move beyond the simple “human-in-the-loop” concept and build practical controls that provide meaningful oversight throughout the AI lifecycle. NIST’s AI Risk Management Framework similarly emphasises clearly defined roles and documented processes for human-AI configurations and oversight.

## What You’ll Learn

✅ Why Human-in-the-Loop Isn’t Enough — Understand why simply adding human approval may not provide meaningful oversight.
✅ Meaningful Human Oversight — Explore when humans should monitor, intervene, approve, override or stop an AI-driven process.
✅ Automation Bias — Understand the risk of people becoming overly dependent on AI recommendations.
✅ Risk-Based Controls — Determine the appropriate level of human control based on the impact and risk of an AI decision.
✅ Approval & Escalation Points — Define which AI actions can proceed automatically and which should require human authorisation.
✅ From Principles to Controls — Turn Responsible AI principles into practical workflows, controls and governance mechanisms.
✅ Accountability & Auditability — Establish clear ownership and maintain evidence of AI decisions, human interventions and approvals.
Human oversight is also becoming an increasingly structured area of AI governance: ISO/IEC 42105, currently in final-draft stage, specifically addresses human control and monitoring of AI systems throughout their lifecycle.

## Who This Is For

C-suite and executive leaders, CIOs, CTOs, CDOs, Chief AI Officers, AI governance professionals, Responsible AI practitioners, risk and compliance teams, technology leaders, AI architects, consultants and professionals involved in building, deploying or governing AI systems.

## Format

90-minute live expert session featuring practical scenarios, human-oversight models, control mechanisms and real-world AI governance considerations, followed by an open Q&A.
The session is built around one critical question:

Having a human in the loop sounds responsible — but is that human actually in control?

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