Introduction
Artificial intelligence (AI)-driven tools have become a cornerstone of business operations. Securing them and building organizational AI resilience is not just about defense; it’s about devising and implementing a proactive strategy and future-proofing the enterprise. The digital landscape is rife with potential threats, and AI systems are particularly vulnerable. The sections below explore how organizations can build resilience in their AI security protocols to not only respond to threats, but prevent them.
Governance
A resilient AI security strategy must include a strong governance framework. This includes setting clear policies for acceptable use and establishing an employee education program, as well as creating a regular review and maintenance cycle for all AI models to ensure they remain fit for purpose and prevent them from becoming outdated or vulnerable.
Observability
The first step in building resilience is establishing deep, comprehensive observability across the AI infrastructure. This involves identifying and cataloging every AI system or technology deployed within the organization. It’s critical to have a clear view of your entire AI ecosystem to monitor activities and detect potential threats in real time.
Beyond Traditional Security Measures
Relying solely on traditional security infrastructure tools is no longer sufficient. Network safeguards and device security cannot protect AI-dependent systems from AI-driven threats. The complexity and sophistication of AI systems, particularly those that include large language models (LLMs) and other generative AI (GenAI) models, demand more advanced solutions. Organizations must adopt AI-specific security measures that are flexible, robust, reliable, scalable, and trustworthy.
Case Study: CalypsoAI
An example of such an advanced solution is the CalypsoAI GenAI security and enablement platform, which offers enterprise-wide observability into all GenAI models on the system, and provides detailed user insights. Its features include:
- Customizable Policy Scanners that protect against the leakage of sensitive, confidential, or proprietary data, and prevent malicious code from infiltrating the system, and help ensure compliance with organizational policies, industry standards, and government regulations.
- Audit Scanners that identify internal threats and issues in real time.
- Policy-Based Access Controls that provide segmented protection at individual and group levels, enhancing security protocols.
- Usage Monitoring and Audit Capabilities that identify who is using the models, when, and for what purposes.
Conclusion
Building resilience in AI security will never be a one-and-done scenario; it must be a continuous process with existing milestones reached and new ones planned. Achieving the level of confidence an organization needs to have in its AI security structure requires a combination of advanced technology and strategic planning, supported by a proactive mindset. By staying ahead of the curve and implementing robust, continually updated security measures, organizations can ensure that they leverage AI technologies safely and effectively, now and in the future.
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Click here to read the previous post in this Security series.
