Why We Invested In Knostic: Leading CISOs' Thesis On AI Security

As AI adoption accelerates across enterprises, the security challenges it introduces are outpacing traditional frameworks. According to SVCI – Silicon Valley CISO Investments, AI access control has emerged as the critical security gap for organizations deploying tools like Microsoft 365 Copilot, Google Gemini, and Glean.

Read the full thesis here

You Should Know: Securing AI with Access Control & Practical Commands

To mitigate AI-driven security risks, enterprises must implement need-to-know access controls and modern security practices. Below are key technical steps and commands to harden AI deployments:

Enforcing Role-Based Access Control (RBAC) in AI Systems

– Linux (IAM Policies)

 Create a custom IAM policy for AI model access 
aws iam create-policy --policy-name AI-Access-Control --policy-document file://ai-policy.json

Attach policy to specific roles 
aws iam attach-role-policy --role-name DataScientist --policy-arn arn:aws:iam::123456789012:policy/AI-Access-Control

– Windows (PowerShell)

 Restrict access to AI tools via Group Policy 
Set-GPPermission -Name "AI_Model_Access" -TargetName "Engineering" -PermissionLevel GpoRead

2. Monitoring AI Model Interactions

Log AI API calls (Linux)

Audit AI service access via auditd 
sudo auditctl -a always,exit -F arch=b64 -S execve -F path=/usr/bin/ai-tool -k ai_access

SIEM Integration (Splunk Query)

index=ai_logs sourcetype=api_access (action=query OR action=generate) | stats count by user, model

3. Zero-Trust for AI Workloads

Kubernetes (Pod Security Policies)

apiVersion: policy/v1beta1 
kind: PodSecurityPolicy 
metadata: 
name: ai-psp 
spec: 
allowedCapabilities: ["NET_ADMIN"] 
runAsUser: { rule: "MustRunAsNonRoot" }

4. Detecting Data Exfiltration via AI

Network Traffic Analysis (Zeek/Bro)

Track outbound connections from AI containers 
zeek -C -r ai_traffic.pcap -e 'print |[timestamp, src_ip, dst_ip, query]|'

What Undercode Say

The rapid evolution of AI demands adaptive security frameworks. Traditional perimeter-based controls fail to address context-aware threats in AI systems. Enterprises must:
– Enforce least-privilege access via `aws iam` or kubectl.
– Monitor model interactions using `auditd` or Splunk.
– Isolate AI workloads via Kubernetes PSPs.
– Block malicious prompts using regex filters in API gateways.

AI security is not optional—tools like Knostic highlight the urgency.

Expected Output:

1. AI access control policies applied via IAM/Kubernetes. 
2. Audit logs of AI tool usage (Linux/Windows). 
3. Alerts for anomalous model queries (SIEM).

Reference: Knostic AI Security