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Generative AI Security:
data loss prevention & emerging risks
The rapid adoption of Generative AI exposes organisations to significant security risks, particularly in Data Loss Prevention (DLP). Data leaks, unauthorised access, and adversarial attacks threaten sensitive information, requiring new strategies to protect valuable digital assets.
The Rise of Data Loss & Exposure Risks in Generative AI
Generative AI’s ability to process vast amounts of enterprise data increases the risk of unintentional data exposure. Key concerns include prompt injection attacks, where malicious actors manipulate AI responses to extract confidential information; data leakage, where AI models inadvertently expose sensitive data; and model hallucinations, where AI generates misleading information that can compromise security.
Cybersecurity managers must secure AI systems to avoid being exploited by insiders or external attackers. Moreover, unauthorised use of AI tools within an organisation—known as "shadow AI"—can further jeopardise security, privacy, and compliance.
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Defensive Strategies for AI Data Protection
To mitigate the risks of data loss, Reply’s cybersecurity experts are helping organisations implement robust defence mechanisms tailored specifically to AI environments. One key strategy involves deploying real-time Data Loss Prevention (DLP) systems, using AI-driven monitoring tools to detect and prevent unauthorised data extraction. Organisations must also ensure that sensitive data is encrypted both in transit and at rest to prevent exposure.
Access control and identity management are equally important, implementing Role-Based Access Control (RBAC) to limit AI’s access to sensitive information. In parallel, aligning also AI deployments with enterprise data protection regulations, such as GDPR, CCPA, and security standards, like ISO 27001, is essential to ensure compliance and minimise risks. Reply’s approach focuses on the implementation of LLM Runtime Defence, an innovative security framework that enforces strict data access controls and prevents malicious exploitation during AI interactions.
Focus on LLM Runtime Defence
LLM Runtime Defence is a real-time security system designed to monitor and regulate AI-generated outputs, ensuring protection from exploitation while maintaining efficiency. It focuses on real-time threat detection, identifying malicious activities and unauthorised data extraction. The system includes adaptive response mechanisms like content filtering and query sanitisation, secure access controls to prevent unauthorised use, and plays a key role in data loss prevention by ensuring that sensitive data is not exposed. Additionally, it safeguards model integrity by preventing adversarial attacks that could manipulate AI responses or introduce bias. The solution offers a multi-layered defence framework to keep corporate AI systems secure.
1. Behavioural Analysis Engines
AI-driven monitoring to flag suspicious activities in real time.
2. Prompt Validation and Filtering
Tools that sanitise user inputs to prevent prompt injection attacks.
3. Dynamic Security Policies
Customisable controls that adapt to each organisation’s needs and industry regulations.
4. Audit Logging & Forensics
Comprehensive tracking and logging of AI interactions for security auditing and threat analysis.
5. Regulatory Compliance Integration
Ensuring that AI-generated content complies with GDPR, CCPA,EU AI ACT, and ISO 27001 standards.
Securing AI: Empowering Cybersecurity with LLM Runtime Defence
With the ongoing evolution of AI, it has become increasingly critical to protect AI-driven systems against data loss. With the growing adoption of generative AI, preventing personal data loss and breaches must become a priority. Reply’s approach to LLM Runtime Defence provides the advisory and the tools necessary to ensure the integrity and safety of business AI applications.