Conference Description
Key Takeaways
- Dedicated conference addressing the security challenges inherent in artificial intelligence systems
- Covers adversarial machine learning, AI risk management, governance frameworks and regulatory compliance
- Designed for security professionals, AI practitioners, technology executives and policymakers
- Explores both securing AI systems and applying AI to enhance cybersecurity capabilities
- In-person format featuring expert-led talks, panel discussions and networking opportunities
Introduction
Secure AI is a specialist conference examining the complex relationship between artificial intelligence and security. The event convenes security professionals, AI researchers, technology leaders and policymakers to address the dual challenge of protecting AI systems from attack while harnessing AI capabilities to strengthen cybersecurity defences. As organisations across finance, government and critical infrastructure accelerate their AI deployments, the need for robust security frameworks and governance structures has become increasingly urgent.
The conference arrives at a pivotal moment for the industry. Regulatory bodies worldwide are introducing new requirements for AI transparency and accountability, while adversarial attacks against machine learning models have grown more sophisticated. Organisations deploying AI now face pressure from multiple directions: ensuring their systems remain resilient against manipulation, demonstrating compliance with emerging regulations, and maintaining stakeholder trust in automated decision-making processes.
About This Event
Secure AI operates as an in-person conference structured around expert-led presentations, panel discussions and dedicated networking sessions. The format prioritises substantive technical and strategic content, bringing together practitioners who are actively working on AI security challenges with researchers advancing the theoretical foundations of the field.
The event positions itself as a forum for knowledge exchange rather than a product showcase. Attendees can expect discussions grounded in real-world implementation experience, covering both the technical mechanisms of AI security and the organisational frameworks required to manage AI-related risks effectively. This combination of technical depth and strategic perspective reflects the multidisciplinary nature of AI security itself, which demands collaboration between data scientists, security engineers, compliance teams and executive leadership.
Securing AI Systems Against Emerging Threats
A central theme of the conference is the protection of AI systems from adversarial attacks. Unlike traditional software vulnerabilities, attacks against machine learning models can be subtle and difficult to detect. Adversarial machine learning encompasses techniques such as data poisoning, where attackers corrupt training datasets to influence model behaviour, and evasion attacks, where carefully crafted inputs cause models to produce incorrect outputs.
These vulnerabilities present particular challenges for organisations deploying AI in high-stakes environments. A compromised fraud detection model, for instance, could allow malicious transactions to pass undetected, while a manipulated content moderation system might fail to identify harmful material. The conference addresses these scenarios by examining both the attack methodologies and the defensive techniques available to security teams.
Building robust and resilient AI systems requires attention throughout the machine learning lifecycle. This includes securing data pipelines, validating model integrity, monitoring for drift and anomalous behaviour in production, and establishing incident response procedures specific to AI systems. The technical complexity of these requirements often exceeds the capabilities of traditional security tools, creating demand for specialised approaches and expertise.
AI Risk Management and Governance Frameworks
Beyond technical security measures, the conference examines the governance structures organisations need to manage AI-related risks systematically. Effective AI governance extends beyond cybersecurity to encompass ethical considerations, regulatory compliance, operational reliability and reputational risk. Chief Information Security Officers and Chief Technology Officers increasingly find themselves responsible for articulating AI risk to boards and ensuring appropriate controls are in place.
The regulatory landscape for AI is evolving rapidly. Jurisdictions worldwide are introducing requirements for algorithmic transparency, impact assessments and human oversight of automated decisions. Compliance officers attending the conference will find discussions relevant to navigating these emerging obligations, particularly in regulated industries where AI deployment intersects with existing data protection and sector-specific requirements.
Trustworthy AI has emerged as a unifying concept in these discussions, encompassing properties such as fairness, explainability, privacy preservation and security. Organisations seeking to build stakeholder confidence in their AI systems must demonstrate attention to all these dimensions, not merely technical performance metrics. The conference provides a venue for examining how these principles translate into practical implementation decisions.
Applying AI to Strengthen Cybersecurity
The relationship between AI and security operates in both directions. While AI systems require protection, they also offer powerful capabilities for enhancing cybersecurity operations. Machine learning models can analyse network traffic patterns to identify anomalies, automate threat intelligence processing, and accelerate incident response through intelligent triage and recommendation systems.
Security teams face an asymmetric challenge: they must defend against all possible attacks while adversaries need only find a single vulnerability. AI-powered security tools can help address this imbalance by processing vast quantities of data, identifying subtle patterns that human analysts might miss, and operating continuously without fatigue. However, deploying AI in security contexts introduces its own risks, as these systems become potential targets for the adversaries they are designed to detect.
This recursive quality—using AI to secure AI—represents one of the more intellectually demanding aspects of the field. The conference explores how organisations can realise the benefits of AI-enhanced security while managing the additional attack surface these systems introduce.
Who Should Attend
The conference serves professionals across multiple roles and seniority levels. Security practitioners will find technical content addressing the specific challenges of protecting machine learning systems. AI researchers and data scientists can engage with the security implications of their work and learn defensive techniques to incorporate into model development processes.
Technology executives, including CISOs and CTOs, will benefit from strategic discussions on AI governance, risk management frameworks and regulatory developments. Policymakers and compliance officers can gain insight into the technical realities that inform effective AI regulation. The cross-functional nature of AI security means that attendees from different backgrounds often find value in perspectives outside their primary domain.
Industries with significant representation include technology, financial services, government and critical infrastructure—sectors where AI deployment is advancing rapidly and the consequences of security failures are particularly severe. However, the principles discussed apply broadly to any organisation developing or deploying AI systems.
The Growing Importance of AI Security Expertise
As AI systems become embedded in critical business processes and infrastructure, the security community faces a significant skills gap. Traditional cybersecurity training does not adequately prepare professionals for the unique characteristics of machine learning systems, while AI practitioners often lack grounding in security principles. Events like Secure AI play an important role in bridging this gap, facilitating knowledge transfer between communities and helping establish shared frameworks for addressing AI security challenges.
The conference contributes to the development of AI security as a distinct discipline, one that draws on machine learning, cybersecurity, risk management and governance while developing its own specialised body of knowledge. For professionals seeking to build expertise in this emerging field, participation offers exposure to current thinking and connection with others working on similar challenges.