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21/08/2026 5:02 pm
TL;DR: LLM risk is not just prompt injection but a full input-output governance problem, where malicious prompts, secret exposure, and unsafe responses can all bypass basic filters if they are not inspected at runtime, according to AccuKnox. The practical implication is that enterprise AI security now depends on dual-layer policy enforcement, auditability, and control over what the model sees and emits.
NHIMG editorial — based on content published by AccuKnox: How to Secure LLM Prompts and Responses with AccuKnox Prompt Firewall
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, sharing sensitive data, and revealing access credentials.
Questions worth separating out
Q: What breaks when prompt-only filtering is used to secure LLMs?
A: Prompt-only filtering misses the second half of the problem: unsafe or sensitive output.
Q: Why do LLMs create risk in identity and access management?
A: LLMs create risk when teams confuse fluent answers with verified security evidence.
Q: How do security teams know whether LLM firewall controls are working?
A: They should look for blocked secrets, logged policy violations, session-linked traces, and a measurable reduction in unsafe prompts and leaked outputs.
Practitioner guidance
- Implement dual-layer policy enforcement Deploy separate controls for prompt inspection and response scanning so secret exposure, injection attempts, and unsafe completions are handled on both sides of the model interaction.
- Block secrets before prompts reach the model Add rules for API keys, tokens, passwords, and code fragments that should never be submitted to an LLM, then quarantine or redact violations for review.
- Require session-linked audit trails Preserve the original prompt, generated output, policy verdict, and session identifier so investigations can reconstruct the full AI interaction chain.
What's in the full article
AccuKnox's full article covers the operational detail this post intentionally leaves for the source:
- Step-by-step configuration of prompt and response policy templates for specific LLM use cases
- Dashboard workflow for reviewing violations, policy triggers, and trace-level evidence
- Implementation details for linking prompts and responses with session IDs
- Practical examples of secret-detection and output-scanning rules used in the firewall
👉 Read AccuKnox's analysis of LLM prompt and response firewall controls →
LLM prompt and response filtering: are your controls keeping up?
Explore further
21/08/2026 5:17 pm
Prompt filtering is only half of AI governance. The article reinforces a basic but often missed point: input controls do not solve output-side leakage or insecure generation. In LLM environments, the risk boundary runs both ways, which means governance must cover what the system accepts and what it emits. Practitioners should treat this as a dual-control problem, not a single filter problem.
A question worth separating out:
Q: Who is accountable when an LLM leaks data after following malicious instructions?
A: Accountability sits with the organisation that granted the model access, connected the tools, and allowed untrusted content into the same decision path. That makes this a governance issue across IAM, security engineering, and application ownership, not a defect that belongs to the model alone.
👉 Read our full editorial: LLM prompt firewall controls close the input-output security gap
