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You don’t need to spend thousands on courses to get started. Here are some free, high-quality resources to build your knowledge:
1. AWS GenAI Security Scoping Matrix
A structured approach to securing GenAI workloads. This three-part series provides a framework to assess and manage risks based on workload type. If you want to understand how to secure AI deployments in the cloud, this is a must-read.
AWS GenAI Security Scoping Matrix
2. NIST AI Risk Management Framework (AI RMF)
Developed by NIST, this tech-agnostic framework helps organizations design, develop, and deploy AI responsibly. Covers key areas like governance, risk assessment, and AI system management—essential for any AI security professional.
NIST AI RMF
3. MITRE ATLAS
A knowledge base of real-world AI attack techniques used against models and systems. If you want to learn how attackers exploit AI and how to defend against them, this is the resource to explore.
MITRE ATLAS
4. OWASP Top 10 for LLMs
Just like traditional web applications, large language models (LLMs) face unique security risks. The OWASP Top 10 for LLMs outlines the most critical threats, including prompt injection, data leakage, and insecure output handling. A must-read for securing AI applications.
OWASP Top 10 for LLMs
Practice Verified Codes and Commands
- AWS CLI Command to Check Security Groups for GenAI Workloads
aws ec2 describe-security-groups --filters Name=tag:Name,Values=GenAI
2. NIST AI RMF Compliance Check Script
<h1>Python script to check AI system compliance with NIST RMF</h1>
import json
def check_compliance(system_config):
with open('nist_rmf_guidelines.json') as f:
guidelines = json.load(f)
for guideline in guidelines:
if guideline not in system_config:
return False
return True
3. MITRE ATLAS Attack Simulation
<h1>Simulate an AI attack using MITRE ATLAS techniques</h1> python3 simulate_attack.py --technique T1234 --target ai_model
4. OWASP LLM Security Scan
<h1>Scan for OWASP LLM Top 10 vulnerabilities</h1> python3 llm_security_scan.py --model my_llm_model --output report.txt
What Undercode Say
AI security is a rapidly evolving field, and staying ahead requires a combination of theoretical knowledge and practical skills. The resources provided in this article, such as the AWS GenAI Security Scoping Matrix and the NIST AI RMF, offer a solid foundation for understanding the risks and best practices associated with AI deployments. MITRE ATLAS provides invaluable insights into real-world attack techniques, while the OWASP Top 10 for LLMs highlights the unique vulnerabilities of large language models.
To further enhance your skills, practice using the provided commands and scripts. For instance, the AWS CLI command helps you audit security groups for GenAI workloads, while the NIST RMF compliance script ensures your AI systems adhere to industry standards. Simulating attacks using MITRE ATLAS techniques and scanning for OWASP LLM vulnerabilities will give you hands-on experience in defending AI systems.
Remember, AI security is not just about tools and frameworks; it’s about adopting a proactive mindset. Regularly update your knowledge, participate in cybersecurity communities, and experiment with new tools. By combining these resources with practical experience, you’ll be well-equipped to tackle the challenges of AI security in today’s digital landscape.
For further reading, explore the following links:
Keep learning, keep practicing, and stay secure!
References:
initially reported by: https://www.linkedin.com/posts/taimurijlal_want-to-learn-ai-security-no-expensive-activity-7301210139812843520-wlE6 – Hackers Feeds
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