Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from io import StringIO
+
+# Sample data for the inline CSV
+data = """
+Section,Title,White_Team_Libraries,Red_Team_Libraries
+1.1,"Responsible and Trustworthy Artificial Intelligence","ai-fairness-360, tensorflow, pytorch","adversarial-robustness-toolbox, tensorflow, pytorch"
+1.2,"Who is This For?","numpy, pandas","numpy, scipy"
+1.3,"Why a Checklist?","pandas, matplotlib","scikit-learn, seaborn"
+1.4,"Not Comprehensive","streamlit, dash","flask, django"
+1.5,"Large Language Model Challenges","transformers, datasets","keras, tensorflow"
+1.6,"LLM Threat Categories","threatmodeler, pytm","mitmproxy, burpsuite"
+1.7,"Artificial Intelligence Security and Privacy Training","owasp, safecode","hackthebox, tryhackme"
+1.8,"Incorporate LLM Security and governance with Existing, Established Practices and Controls","compliance-checker, policy-sentry","sqlmap, nmap"
+1.9,"Fundamental Security Principles","secure-code-toolkit, bandit","metasploit, pwntools"
+1.10,"Risk","risk-frameworks, gordon","exploit-db, cve-search"
+1.11,"Vulnerability and Mitigation Taxonomy","vuln-code-db, flawfinder","yara, volatility"
+2,"Determining LLM Strategy","strategic-planning, mindmaps","red-team-toolkit, blue-team-toolkit"
+2.1,"Deployment Strategy","docker, kubernetes","ansible, puppet"
+3.1,"Adversarial Risk","cleverhans, foolbox","gym, reinforcement-learning"
+3.2,"Threat Modeling","pytm, sea-sponge","attackflow, evilginx"
+3.3,"AI Asset Inventory","asset-inventory, snipe-it","openvas, qualys"
+3.4,"AI Security and Privacy Training","security-training, cybrary","offensive-security, sans"
+3.5,"Establish Business Cases","business-model-canvas, leanstack","swot-analysis, pestle-analysis"
+3.6,"Governance","corporate-governance, iso-standards","cobit, itil"
+3.7,"Legal","legal-docs, law-libraries","case-law-search, legal-hack"
+3.8,"Regulatory","regulatory-compliance, gdpr","fcc-tools, eu-data-protection"
+3.9,"Using or Implementing Large Language Model Solutions","model-deployment, ml-ops","shadow-it, devops-tools"
+3.10,"Testing, Evaluation, Verification, and Validation (TEVV)","test-automation, quality-assurance","bug-bounty, fuzzing"
+3.11,"Model Cards and Risk Cards","model-card-toolkit, aboutml","threat-modeling, risk-analysis"
+3.12,"RAG: Large Language Model Optimization","rag-tokenizer, huggingface-transformers","genetic-algorithms, particle-swarm"
+3.13,"AI Red Teaming","red-team-framework, adversary-simulation","kali-linux, pen-test-tools"
+"""
+
+# Creating a DataFrame
+df = pd.read_csv(StringIO(data))
+
+# Streamlit app
+st.title('AI System Defense Strategy')
+
+# Display DataFrame in the app
+st.write(df)
+
+# Instructions or additional app features can go here
+st.markdown("""
+This table includes sections relevant to defending AI systems, along with recommended Python libraries for both white team (defense) and red team (offensive security) developer leads. Use the information to guide the development and testing of responsible and secure AI systems.
+""")