Ensuring Data Security in the Age of AI

Organisations across Australia and New Zealand (ANZ) face challenges in managing and securing their valuable data. While data is often described as the new gold, valuable, full of potential, and increasingly sought after. But a more fitting analogy might be uranium: powerful, volatile, and best handled with strict controls.

Organisations rely on AI to drive innovation and efficiency; they also address the challenges that come with it. In 2024, 95% of organisations faced challenges in AI implementation, essentially due to data readiness and information security. Storing years of data increases exposure to cyberthreats and insider risks.

Managing huge amounts of data requires lifecycle management and regulatory compliance with legislative requirements like the Public Records Act, Australia’s Privacy Act 1988, New Zealand’s Privacy Act 2020, and industry-specific regulations like the Australian Prudential Regulation Authority (APRA), and proactive Data Security Posture Management (DSPM) is now essential.

This blog covers a new approach to information security that addresses the unique challenges presented by AI technologies.

The Intersection of AI, Data Security, and Information Management

According to Forrester, 60% of Asia Pacific organisations are localising AI models to address regional demands, regulatory compliance, and linguistic diversity. The success of such initiatives depends heavily on the maturity of information management strategies.

Organisations with strong data governance are 1.5 times more likely to realise the benefits of AI early. That’s because effective information management through policies, controls, and lifecycle strategies lays the groundwork for responsible data use, privacy protection, and risk mitigation.

The scale of the threat is evident. The Australian Cyber Security Centre (ACSC) recorded 527 data breach notifications from January to June 2024, the highest in three and a half years. Of those, 67% were due to malicious or criminal activity, often targeting data-rich environments.

New Regulatory Pressures in Australia and New Zealand

In response to rising threats, ANZ governments are tightening regulations:

Australia’s Privacy Act Reforms

Stricter penalties: Up to 10% of annual turnover for serious breaches
Expanded personal information scope: Now includes technical and behavioural data
Stricter consent: Explicit, informed consent for data collection and use
Enhanced rights: Greater transparency, access, and data deletion rights
Mandatory breach reporting: Shorter notification windows
Privacy by design: Security embedded from the outset

New Zealand’s Updated Privacy Framework

Cross-border safeguards: Stricter controls for international data transfers
Risk assessments: Mandatory for high-risk data processing activities
Accountability: More detailed compliance reporting and governance expectations

These reforms make it imperative for organisations to reassess how they collect, store, and process data, particularly when deploying AI.

Managing Data Sensitivity in AI Environments

AI systems process vast amounts of sensitive data, from personal information to confidential business intelligence. We’ve seen large-scale data breaches with organisations like MediSecure, an Australian health organisation that holds sensitive medical information and dispenses e-prescriptions. The incident disrupted critical services and led the company to seek government assistance.

This is not an isolated event. It underscores that even well-established organisations are vulnerable and that AI deployments require security from day one.

To meet this need, organisations are turning to Data Security Posture Management (DSPM) to:

Identify and classify sensitive data across platforms
Apply tailored security controls
Monitor data access patterns and detect anomalies
Enforce compliance and automate responses
Proactively reduce the data footprint to lower risk exposure

New Security Roles and Continuous Vigilance

AI adoption is driving the creation of new security functions. These roles are responsible for:

Assessing AI model vulnerabilities
Creating AI-specific security policies
Managing risk exposure without altering source data
Orchestrating incident response for AI-related threats

Security isn’t static. It’s a continuous conversation. Organisations must evaluate risk tolerance, assess posture regularly, and align security strategy with business goals. Creating a culture of shared responsibility is important for maintaining resilience.

Automation: Scaling Data Security for AI

Manual security processes can’t keep pace with the speed and scale of AI. Automation is now critical for safeguarding sensitive data across environments.

Leading organisations are using AI-driven tools to:

Map access permissions and identify exposure hotspots
Conduct real-time threat detection and incident response
Enforce data handling policies based on risk
Visualise sensitive data through interactive dashboards and heatmaps

According to Cybersecurity Ventures, adoption of advanced threat detection tools surged by 35%. Gartner also predicted that 70% of organisations will have integrated AI-driven threat intelligence systems by 2025, enhancing their ability to identify and mitigate threats before they manifest into major incidents.

Elevating Data Security Through Governance and Quality

AI systems are effective as the data that feeds them. Inaccurate or outdated information leads to flawed insights and poor decision-making. More importantly, it creates unnecessary security risk.

To ensure quality and governance, organisations should:

Automate the detection of outdated or redundant data
Implement metadata and lifecycle management
Create governance frameworks aligned to AI use cases

Gartner estimates that poor data quality accounts for 30% of security-related costs, with losses averaging $14.2 million annually. Proactively managing data quality not only improves AI performance but also significantly reduces an organisation’s attack surface.

Toward a Holistic AI Security Strategy

The most secure organisations in the AI era are those that adopt a holistic approach, integrating information governance, compliance, data sensitivity management, and automation.

This means:

Embedding privacy and security in AI design
Regularly updating policies to match legal changes
Investing in AI-specific security roles and technologies
Cultivating a security-first culture across the organisation

The goal is not just to defend against threats but to build trust with customers, regulators, and stakeholders.

Ready to strengthen your data security and unlock the full potential of AI? Get in touch with us today to explore solutions tailored to your organisation’s needs.