I am seeing it first hand. AI systems are gaining access to our tools, accounts, and personal data faster than most teams expected. That is a real risk. Even as someone who pushes agentic AI hard, I will say clearly that there is more we do not know than what we do. I also believe progress beats paralysis. We should push forward and explore these risks with eyes open, good controls, and measurable checks.

AI Governance for Operations: Risk-First Program Design

Security programs that work share one pattern: align people, process, and platform, then measure outcomes the business cares about. Start from your SOPs, not from prompts. Give each agent a role account with the smallest scopes it needs. Use an orchestrator that calls APIs, enforces approvals, and stamps every step with a trace ID so you can replay decisions later. That is how Ops, IT, and Security stay on the same page.

NIST AI Framework for Execs and Engineers

Use the NIST AI Risk Management Framework as shared language with risk teams. Map work to Govern, Map, Measure, and Manage. It gives you a concrete way to document risks, controls, and monitoring without inventing a new vocabulary for every workflow. Pair it with your existing security baselines and change-control processes.
NIST AI RMF 1.0 (PDF) · NIST AI RMF

My Stance on Risk and Progress

Acknowledge the uncertainty. Set guardrails early. Then ship one workflow in production and learn from real telemetry. The aim is not zero risk. It is bounded, observed, and continuously reduced risk while cycle time, error rate, and cost per transaction improve.

Prompt Injection and Insecure Output Handling in Real Workflows

Prompt injection shows up wherever models read untrusted input: tickets, emails, docs, web pages, even data pulled by retrieval. It can redirect an agent, leak data, or trigger risky tools. Treat it as a top risk, not a fringe threat. OWASP’s GenAI project lists prompt injection and insecure output handling as core items with clear mitigations. Microsoft’s recent write-ups describe defense-in-depth patterns that blend deterministic checks with probabilistic detectors. Use both.
OWASP GenAI: LLM01

What this looks like in the stack. Attack paths include user forms, retrieved PDFs, third-party connectors, and logs. Do not execute model output blindly. Validate types, enforce allow and deny lists for tools, and require approvals at thresholds. Keep a kill switch and apply rate limits and budgets so a compromised session cannot spiral. These controls map directly to OWASP LLM01 and LLM02.
OWASP Top 10 for LLMs (PDF)

Controls You Can Ship This Week

Harden prompts, then verify at runtime. Never rely on templates alone.
Typed tool schemas with strict argument validation. Reject free-form commands.
Isolation boundaries for untrusted content. Never mix raw retrieval with privileged prompts.
Budget guards for tokens, calls, and money. Add circuit breakers and anomaly alerts.
One-click review band for medium-risk actions. Escalate with reason codes outside the band.

RAG Security and Secure RAG for Enterprise Search

Retrieval is powerful and it expands your attack surface. Poisoned or crafted documents can smuggle adversarial instructions or false facts into model context. Build retrieval like a production system, not a demo. Keep indexes clean, track provenance, and gate access to vector stores and caches. Recent security research calls out RAG-specific risks and concrete mitigations, including index hygiene, source verification, and rollback plans for tainted corpora.
arXiv: RAG security

Index Hygiene, Provenance, and Access Control

Curate and sign sources. Ban unknown write paths. Quarantine new feeds until checked.
Provenance tags in the index so you can trace and purge on demand.
Access control on vector stores equal to your data warehouse, not your wiki.
Grounded answers with citations. Require the agent to show retrieved sources for sensitive outputs.

Data Loss Prevention for AI and Data Exfiltration LLM Risks

Assume anything passed to a model or a tool could leak if controls fail. Minimize by default, mask PII and secrets before prompts, and log masked versions only. If you use third-party providers, review data handling and retention, and consider private connectivity or on-prem where sensitivity requires it. ISO/IEC 42001 gives an AI management system lens to line this up with your broader governance program.
ISO/IEC 42001 · KPMG overview

Privacy by Design and Provider Logging Reality

Selective redaction at the prompt, tool, and storage layers.
Tokenization for direct identifiers.
Ephemeral keys and short-lived sessions.
Broker patterns and private gateways when models sit outside your VPC.
Third-party reviews that cover logging, training use, and retention. Treat these as procurement requirements, not optional questions.

Model Hallucination Detection and Continuous Verification

Hallucinations are not only a UX problem. In operations they create audit gaps, wrong actions, and rework. Build evaluation into the system. Use red teams, adversarial test sets, and regression suites that reflect your real edge cases. Research on hallucination detection in decision workflows shows practical techniques that combine retrieval checks, structured validators, and human review for high-impact steps.
arXiv: Hallucination detection

Red Teaming, SLOs, and Stage-Gated Rollout

Red team loops before and after launch. Track findings like vulnerabilities.
Operational SLOs for latency, accuracy, safety, and straight-through rate. Alert and roll back if they drift.
Stage gates tied to metrics. Expand coverage only when KPIs and incident rates meet thresholds. Google and industry playbooks recommend phased adoption with clear risk reviews and policy as code.
Google Cloud CISO perspectives

Decision checklist to start fast.

Pick one workflow with clear pass criteria and a clean system of record.
Map risks with NIST AI RMF. Name controls, owners, and logs.
Ship with guardrails: approvals, typed tools, budgets, and replay.
Verify with red teams and regression suites.
Expand only when SLOs and KPIs hold.

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Sources and Further Reading

OWASP GenAI Security Project and Top 10 for LLM applications: Overview · PDF
NIST AI Risk Management Framework 1.0: PDF
Microsoft guidance on defending against indirect prompt injection: MSRC blog
Research on RAG security and poisoning risks: arXiv
Hallucination detection techniques: arXiv
ISO/IEC 42001 overview for AI governance programs: ISO

LLM Security: Enterprise Risks, Frameworks, and Secure Deployment Patterns