Known Risks in AI are stacking up.

How Clean is your AI?

Organizations are rushing to AI applications, Conversational Agents, Enterprise Data  Integration, AI Customer service Chatbots.

LLM APIs from major cloud providers (like Google, AWS, Azure via their Vertex AI, Bedrock, or Azure AI services) are powerful but have limits.

 

Hallucinations (making stuff up), Knowledge cutoffs, bias, lack of true reasoning, and context window limits; “do-not-use” areas involve high-stakes decisions (medical, financial), sensitive data handling without robust masking, critical security functions (like real-time threat detection), and tasks requiring verifiable, up-to-the-minute facts due to risks of misinformation, privacy leaks, and unreliable outputs. 

 

Key challenges also include prompt injection, data privacy, and difficulty with complex logic or structured data. 

 

Top Limitations & Risks

  • Hallucinations & Factual Errors: Models generate plausible but false information, especially outside training data.
  • Knowledge Cutoff: Lack awareness of events or data after their last training date.
  • Bias & Fairness: Inherit biases from training data, leading to unfair outputs.
  • Security Vulnerabilities: Susceptible to prompt injection, where malicious instructions manipulate behavior.
  • Context Window Limits: Forget details from earlier in long conversations/documents.
  • Lack of True Reasoning/Understanding: Predict words, not truly comprehend; struggle with complex logic, math, or abstract concepts.
  • Data Privacy: Potential for sensitive data to leak if not properly managed.
  • Structured Data Issues: Can struggle with precise counting or understanding row/column relationships in tables/CSV. 

 

“Do-Not-Use” / High-Risk Scenarios

 

  • Critical Decision Making: Medical diagnoses, financial advice, legal judgments where accuracy is paramount.
  • Real-time, High-stakes Operations: Autonomous systems, critical infrastructure monitoring.
  • Handling Highly Sensitive PII/PHI: Unless heavily filtered/masked; risk of leakage or misuse.
  • Tasks Requiring Up-to-the-Minute Facts: News summaries, stock market analysis (without RAG/search integration).

 

  • Security Auditing/Code Execution: Agentic tools pose risks of prompt injection leading to harmful actions.
  • Replacing Core Human Functions: Can’t replace genuine empathy, complex ethical judgment, or creativity. 

 

Mitigation Strategies (Implicit “Do-Use-With” Guidance)

  • Retrieval-Augmented Generation (RAG): Combine with external, verified data sources.
  • Human-in-the-Loop: Always involve human review for critical outputs.
  • Prompt Engineering & Guardrails: Use robust prompts and input validation to prevent misuse.
  • Data Governance: Implement strict policies for sensitive data. 

AlertAI AI security governance gateway platform sets  #1 Gold Standard 2025  in AI security, AI Governance with implementing 500+ automated AI Policies from Security to compliance, Governance to Cost, Performance.

AlertAI helps organizations meet goals to comply with GDPR, CCPA, NIST AI Risk Management Framework, ISO/IEC 42001, FedRAMP, DORA, HIPAA, FINTRAC, AI governance laws.

How AlertAI can help organizations with:

  • Real-time AI access, AI RBAC, AI ABAC, Redaction
  • Security Reconnaissance, Compliance Provenance, AI Traceability
  • Agentic and AI audits
  • AI risk assessments
  • Automate Compliance
  • Compliance AI Agent
  • Regulatory Alignment
  • Ensure your AI is governed as per frameworks like the NIST AI RMF and federal AI legislations.