Safeguarding Data in the Era of AI

Safeguarding Data in the Era of AI

As AI becomes integral to innovation and efficiency, it introduces unique security challenges. In 2024, 95% of organisations encountered hurdles in AI implementation, primarily due to data readiness and information security concerns. Organisations with extensive data archives are particularly susceptible, as each stored data point could be a target for cyber threats or insider risks.



Effective management of vast data volumes demands robust lifecycle management and adherence to legislative mandates such as Australia’s Privacy Act 1988, New Zealand’s Privacy Act 2020, and industry-specific regulations like those from the Australian Prudential Regulation Authority (APRA). Proactive Data Security Posture Management (DSPM) is essential to protect sensitive information and mitigate risks.

The Crucial Link Between Data Security and Information Management

Forrester reports that 60% of Asia Pacific firms will localise AI using regionally trained language models, influenced by diverse customer needs, regulatory challenges, and linguistic nuances. Organisations with mature information management strategies are 1.5 times more likely to realise early benefits from AI implementation compared to those with less robust approaches.

Information management lays the groundwork for effective data security by establishing policies, procedures, and systems for creating, utilising, sharing, and disposing of information assets.

Notably, the Office of the Australian Information Commissioner (OAIC) and the Australian Cyber Security Centre (ACSC) reported 527 data breach notifications from January to June 2024—the highest in three and a half years. Malicious and criminal attacks accounted for 67% of these breaches, with 57% being cyber security incidents. This underscores the growing reliance on digital tools and the sophistication of cybercriminals leveraging emerging technologies like AI to circumvent traditional defences.

Upcoming reforms to the Privacy Acts in ANZ impose stringent obligations to prevent data breaches. Key changes in Australia include:

  • Significantly increased penalties for serious privacy breaches (up to 10% of annual turnover)
  • An expanded definition of personal information, explicitly encompassing technical data and online identifiers
  • Stricter consent requirements, demanding clear, specific, and timely consent for data collection
  • Enhanced individual rights, including broader access, correction, and deletion of personal information
  • Mandatory data breach notifications with tighter timelines and reporting requirements
  • A ‘privacy by design’ mandate, requiring the integration of privacy protections from the inception of systems and processes

Similarly, New Zealand has updated its Privacy Act framework, building upon the 2020 reforms with additional measures:

  • Strengthened cross-border data protection, implementing new restrictions on international data transfers
  • Expanded regulatory powers for the Privacy Commissioner
  • Mandatory risk assessments for high-risk data processing activities
  • Enhanced accountability measures, including more detailed record-keeping requirements

ANZ organisations must now reassess and update privacy policies, data collection practices, and security measures to comply with these stricter requirements, highlighting the intrinsic connection between AI and data security.

Navigating Data Sensitivity in AI Contexts

Modern customers and employees anticipate security by design. AI systems process vast amounts of sensitive data, ranging from personal information to confidential business intelligence. The large-scale data breach at MediSecure, an Australian health organisation handling sensitive medical information and e-prescriptions, affected nearly half of the Australian population. The company required government assistance to manage its affairs, assets, and liabilities.

This incident underscores the profound impact cyberattacks can have, extending beyond privacy concerns and financial repercussions. Targeting a healthcare sector that provides essential, time-sensitive services, where downtime can cause significant operational disruptions, serves as a stark reminder that no organisation is immune. It’s not a matter of “if” but “when.”

As AI systems advance, business strategies to protect sensitive information must evolve correspondingly. Effective DSPM enables organisations to:

  • Identify and classify sensitive data across all storage locations
  • Apply appropriate security controls based on data sensitivity
  • Monitor access patterns and detect anomalies
  • Enforce compliance with regulatory requirements
  • Automate security responses to potential threats

Implementing DSPM solutions allows ANZ organisations to gain visibility into their sensitive data landscape and proactively address security risks before they escalate into breaches.

Emerging Security Roles and Ongoing Dialogue

The rise of AI has introduced new security roles within organisations, focusing on analysing risk exposure and managing data security without altering tags or permissions. Security leaders must prioritise resilient data security to prevent data loss or breaches, leveraging new tools and technologies to gain a deeper understanding of organisational risks. These specialists assess AI system vulnerabilities, develop AI-specific security policies, and coordinate responses to AI security incidents.

As the threat landscape continually evolves, organisations must engage in ongoing security discussions to stay ahead of potential risks. This involves regularly assessing security postures, identifying risk acceptance criteria or tolerance levels, and reallocating resources for security initiatives and compliance with evolving regulatory requirements. Fostering a culture of security ensures that AI initiatives are resilient and secure.

For executives in ANZ organisations, these new security capabilities represent not just a cost centre but a strategic investment. By maintaining continuous dialogue around security concerns, organisations can develop a shared understanding of risks and align security initiatives with broader business objectives.

Automating Data Security for Scalability and Efficiency

Manual approaches to data security are insufficient to keep pace with the data volumes in AI environments. Automation has become essential for effective protection. Cybersecurity Ventures reports a 35% increase in the adoption of advanced threat detection tools. Gartner predicts that by 2025, 70% of organisations will have integrated AI-driven threat intelligence systems, enhancing their ability to identify and mitigate threats proactively.

Organisations can conduct risk assessments to determine who has access to sensitive data, monitor access patterns, and identify potential threats from external entities. By aggregating highly exposed content with sensitive information types, organisations can create a heatmap of at-risk data across their systems. This facilitates rapid and efficient resolution of data visibility concerns, ensuring the protection of sensitive information.

These automated capabilities enable security teams to focus on strategic initiatives rather than routine monitoring, significantly enhancing operational efficiency.

Enhancing Data Security Through Quality and Governance

AI systems depend on accurate and current data to generate meaningful insights. Outdated or obsolete data can lead to incorrect recommendations and decisions. Organisations must automatically identify and address outdated content to maintain the accuracy of AI outputs. This involves archiving or deleting obsolete data and ensuring AI systems access high-quality, relevant information.

Gartner estimates that poor data quality costs organisations an average of $14.2 million annually, accounting for approximately 30% of security-related expenses. To bolster data security, organisations should:

  • Implement automated data quality checks
  • Establish clear data governance frameworks
  • Develop metadata management systems
  • Create lifecycle management policies

By prioritising data quality and governance, organisations establish a security foundation that significantly reduces their attack surface while enabling AI systems to operate within defined security parameters. This approach transforms lifecycle management from a support function into a strategic security asset, directly contributing to organisational cyber resilience.

A Comprehensive Approach to AI Data Security

As AI adoption accelerates across ANZ, security challenges will continue to evolve. Organisations that adopt a comprehensive approach—integrating information management, data sensitivity governance, and data readiness—will be best positioned to leverage AI’s benefits while mitigating its risks.

The journey toward robust data security in the AI era requires ongoing commitment, investment, and adaptability. By focusing on these critical areas, ANZ organisations can establish a foundation for secure and responsible AI deployment.

Discover how AvePoint’s AI Security and Confidence solutions can assist your organisation in implementing robust data security and information management practices to support your AI initiatives.

For more information, visit AvePoint’s AI Security and Confidence Solutions.

Safeguarding Data in the Era of AI


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