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Top 5 Steps For Securing LLMs and Critical Data

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In today's generative AI landscape, traditional security techniques fall short in protecting modern AI applications. Cybersecurity teams are tasked with the challenge of enabling business innovation while mitigating threats to LLM applications and safeguarding sensitive company data. The rapid adoption of generative AI, coupled with the rise of shadow AI, unknown vulnerabilities, and emerging threats, presents significant security, privacy, and compliance risks.

Watch our in-depth webinar to explore top 5 AI Security steps recommended to bolster your GenAI defenses.

Key Takeaways:

  • Understanding AI Security Challenges: Discuss shadow AI, OWASP Top 10 Threats for LLMs, data mapping, and sensitive data exposure risks.
  • Implementing LLM Firewalls: Protect your prompts, data retrieval, and responses from attacks.
  • Enforcing Data Entitlements: Prevent unauthorized data access by users of GenAI applications.
  • Enforcing Inline Enterprise Controls: Safeguard sensitive data from misuse in model training, tuning, and RAG (Retrieval Augmented Generation).
  • Automating Compliance: Streamline adherence to emerging data and AI regulations.

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