Securing AI in the Era of Local Inference: Why the Shift Matters
Over the past year and a half, the focus for CISOs in handling generative AI has been on controlling browser activity. However, a new trend is emerging that challenges this approach.
Large language models (LLMs) are increasingly being utilized on individual endpoints, offline, without the need for external API calls. This shift, also known as Shadow AI 2.0 or the “bring your own model” (BYOM) era, poses new challenges for security teams.
Traditional methods of monitoring network traffic are becoming obsolete as sensitive data is processed locally, beyond the reach of network security tools. This presents a new set of risks that need to be addressed.
Why Local Inference is Gaining Traction
The accessibility of high-performance accelerators, advancements in quantization techniques, and the ease of model distribution have made running complex models on personal devices a common practice among technical teams.
This shift to local inference has implications for security, as activities happening on endpoints may go unnoticed by traditional security measures.
The Risks of Local Inference
While data may not be leaving the device, the risks associated with local inference are related to integrity, compliance, and provenance.
1. Code and Decision Contamination
Local models, often adopted without proper vetting, can introduce vulnerabilities into internal systems without detection.
2. Licensing and IP Exposure
The use of unapproved models can lead to licensing violations and intellectual property issues.
3. Model Supply Chain Exposure
Endpoints storing large model artifacts can be vulnerable to security threats if proper governance and oversight are lacking.
Mitigating the Risks of BYOM
To address the challenges posed by local inference, organizations need to implement endpoint-specific controls and create a curated internal model hub.
By focusing on endpoint governance, providing a centralized model repository, and updating policy language to encompass local model usage, organizations can better manage the risks associated with BYOM.
Redefining Security in the Age of Local Inference
As AI activity shifts towards individual endpoints, CISOs need to adapt their security strategies to focus on controlling artifacts, ensuring provenance, and enforcing policies at the endpoint level.
By acknowledging the shift towards local inference and implementing targeted security measures, organizations can effectively secure their AI infrastructure without hindering productivity.
Written by Jayachander Reddy Kandakatla, Senior MLOps Engineer
