When AI Runs Organisations: Who Protects Data, Infrastructure & Decisions?

Singapore Standard Time 8:00 AM - 11:10 AM (GMT+08:00) Protecting Data, Automating Operations and Building Infrastructure That Never Stops • AI is moving into consequential work. Agents are beginning to modify records, trigger actions, manage workflows and interact with systems organisations cannot afford to lose. • An AI failure is no longer just an AI problem. Compromised agents, corrupted data or infrastructure failure can spread across applications, workflows and critical services. Stopping the agent cannot undo its changes. • Recovery must be built into the AI operating model. Organisations need more than prevention - they need immutable recovery points, a trusted state to return to and rapid recovery without rebuilding the environment from scratch. • AI resilience requires a closed loop. A secure foundation, protected data, intelligent orchestration and verified recovery must work together for AI to operate with consequence. The question is no longer simply “Can AI act?” - it is “When AI acts, fails or is compromised, can the enterprise contain the impact and recover with certainty?” “AI can act at machine speed. Your resilience has to respond at the same speed.” OpenGov Asia When AI Runs the Organisation: Who Protects the Data, Infrastructure and Decisions? AI is moving beyond generating insights to taking action. Agents are beginning to modify records, trigger workflows and interact with business-critical systems at machine speed. But greater autonomy brings greater consequences. A compromised agent, excessive permissions, corrupted data or infrastructure failure can disrupt operations far beyond the AI system itself. Stopping an agent does not undo what it has already changed. The challenge is no longer just preventing AI failure. It is preventing exposure, containing threats and recovering with confidence when something goes wrong. This is the emerging challenge of Resilient AI. Organisations need an operating environment that combines prevention, detection, response and recovery. That means controlling what AI can access and change, identifying abnormal behaviour before disruption spreads, and maintaining a trusted state to which critical systems can return. Building Resilience Across the AI Operating Chain Secure Infrastructure What is AI running on? AI needs a secure, dependable foundation to support critical operations. This starts with governing systems, identities and agent permissions through least-privilege access, limiting exposure before an agent acts. Protected Data What happens to the data AI can access and change? As AI interacts with operational data, business information and models, data protection becomes inseparable from AI resilience. Immutable recovery points preserve a trusted state that cannot simply be overwritten by error, attack or compromise. Prevention, Detection and Orchestrated Response Can organisations detect what is happening and act before disruption spreads? Organisations need to manage exposure, detect anomalies and coordinate responses across infrastructure, applications, data and workflows. Connecting these capabilities enables teams to contain threats at machine speed, understand what acted and why, and establish clear accountability for consequential actions. Verified Recovery Can organisations restore operations to a state they can trust? Prevention must be paired with recovery. When data is altered, systems are compromised or workflows are disrupted, organisations need to restore affected environments to a known, trusted point in time and validate that state before operations resume. Together, these capabilities create a resilient AI operating model: prevent exposure, protect critical data, orchestrate the response and verify recovery. From AI Autonomy to AI Resilience The next stage of AI adoption will depend not simply on how much autonomy organisations give their agents, but on how effectively they control the consequences. Can organisations limit what AI is authorised to do, detect when something goes wrong, contain the impact and recover without prolonged disruption? Resilience must be built into how AI is designed and operated - not left until after an incident. Prevent at machine speed. Protect what matters. Contain the impact. Recover with confidence. Through expert perspectives and interactive discussion, attendees will explore how to: • Prevent exposure by governing identities, systems and AI agents through least-privilege access and appropriate controls. • Protect critical data with immutable recovery points that preserve a trusted state against corruption, compromise and unintended changes. • Detect and contain threats by connecting exposure management, anomaly detection and orchestrated response across the enterprise. • Establish accountability by understanding what acted, why it acted and how teams can intervene when AI behaves unexpectedly. • Recover with confidence by restoring affected systems and data to a verified state, rather than simply restarting operations. • Balance autonomy with control by designing prevention and recovery into the AI operating model from the outset. As AI moves from generating answers to taking action, the executive priority is clear: ensure that greater autonomy does not translate into greater exposure. The question is not simply whether AI can act, but whether your organisation can prevent, contain and recover from the consequences when it does. Critical Takeaways: • UNDERSTAND why AI resilience must extend beyond the agent itself to the infrastructure, data and workflows it can affect. • PROTECT critical AI data through immutable recovery points that preserve a trusted state against corruption, compromise and unintended change. • ORCHESTRATE detection and response by connecting anomalies, operational workflows and recovery actions into a controlled sequence. • VERIFY recovery by restoring data and systems to a known, trusted state before critical operations resume. • DESIGN AI for resilience from the outset, so greater autonomy does not create greater exposure without a corresponding ability to contain and recover. Who Should Attend: • Chief Information Officers • Chief Technology Officers • Chief AI Officers • Chief Digital Officers • Chief Data Officers • Chief Operating Officers • Chief Information Security Officers • Chief Risk Officers • Heads of AI & Automation • Heads of Infrastructure & Cloud • Heads of Data & Analytics • Heads of IT Operations • Directors & Heads of Enterprise Architecture • Directors & Heads of Infrastructure • Directors & Heads of AI & Data • Directors & Heads of Digital Transformation • Directors & Heads of IT Operations