Questioning the Reliability: Is It Wise to Hand Over Business Confidentialities to Artificial Intelligence?
In the rapidly evolving digital landscape, Artificial Intelligence (AI) is increasingly being integrated into various sectors, including security audits. AI-based audit tools are becoming a common sight in many major companies, offering round-the-clock monitoring and immediate risk alerts.
One such advocate for this technology is Priya Mohan, who works with KPMG's Cybersecurity & Technology Risk division and is also associated with LinkedIn Learning's AI Courses.
While AI tools provide numerous benefits, such as reducing costs by automating manual data sifting and streamlining compliance reporting, they also require deep read-level access to sensitive areas of a company's stack. To ensure data security when using AI for security audits, a multi-layered approach is essential.
This approach includes strict data privacy controls, robust governance, continuous monitoring, and compliance with relevant regulations. Key strategies include:
- Data Privacy by Design: Integrating privacy and security measures into AI system development from the outset, using techniques like data anonymization, encryption, and data minimization to protect sensitive information.
- Comprehensive Data Governance: Maintaining strong governance practices such as data inventory and mapping, defining data retention policies, controlling access permissions, and ensuring proper consent management for data usage.
- Regular Audits and Compliance Assessments: Conducting thorough and frequent audits of AI tools to assess their functionality, data privacy risks, and compliance with regulations like GDPR, HIPAA, or CCPA.
- Continuous Monitoring with AI-Enabled Detection: Employing AI-driven, real-time monitoring systems to continuously scan for anomalies, potential threats, unauthorized access, and suspicious behaviour patterns.
- Transparency and Explainability: Implementing explainable AI techniques to understand and document AI decision processes, enhancing accountability and ensuring systems behave as intended without hidden biases or vulnerabilities.
- Security Features in AI Databases: Utilizing built-in security measures in AI databases, such as encryption, audit logging, data masking/redaction, anomaly detection, and automated data retention policies.
- Employee Training and Vendor Controls: Educating employees on AI privacy and security best practices and assessing the security posture of third-party vendors involved in AI data processing or management.
By adopting these practices, companies can balance proactive protections, regulatory compliance, and trustworthy AI use within security audits, safeguarding sensitive data while enabling organizations to leverage AI's benefits securely and ethically.
Moreover, AI can scan large amounts of data quickly to flag anomalies and unusual behaviours, automate the collection of evidence from logs, emails, and transactions, and continuously monitor systems to catch threats as they emerge. Some AI security tools may use a shared SaaS model where user data is anonymized and aggregated, but still used to fine-tune the vendor's detection capabilities.
In conclusion, the integration of AI into security audits offers numerous benefits, but it is crucial for companies to implement a multi-layered approach to ensure data security. By doing so, they can harness the power of AI while minimizing potential risks.
Priya Mohan, who works with KPMG's Cybersecurity & Technology Risk division and is also associated with LinkedIn Learning's AI Courses, emphasizes the benefits of AI for security audits but stresses the importance of a multi-layered approach to maintain data security.
Businesses can protect sensitive information using techniques like data privacy by design, which integrates privacy and security measures into AI system development from the outset. Comprehensive data governance, including data inventory and mapping, defining data retention policies, controlling access permissions, and ensuring proper consent management for data usage, is also crucial.
To further secure AI tools within companies, regular audits and compliance assessments, continuous monitoring, and AI-enabled detection should be employed. This can help organizations balance proactive protections, regulatory compliance, and trustworthy AI use within security audits, while ensuring sensitive data is safeguarded.
