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01/08/2026 10:48 pm
TL;DR: LLM security covers access control, policy enforcement, logging, and configuration hygiene, while LLM protection operates inline to stop prompt injection, jailbreaks, and insecure output handling during execution, according to Impart Security. The split matters because governance and observability do not equal enforcement when attacks unfold across a live session.
NHIMG editorial — based on content published by Impart: LLM Security vs. LLM Protection, Why Teams Need to Secure LLMs at Runtime
Questions worth separating out
Q: How should security teams govern LLMs that can call tools or run code?
A: Security teams should govern them as privileged workloads, not as chat interfaces.
Q: Why do static LLM controls fail against indirect prompt injection?
A: Because the malicious instruction often enters through trusted retrieved content or a legitimate data source rather than the prompt itself.
Q: What do security teams get wrong about LLM monitoring?
A: They often monitor for bad prompts or unsafe outputs without watching the actions the model attempts to take.
Practitioner guidance
- Separate governance controls from enforcement controls Inventory which controls only define acceptable use, which only record activity, and which can actually stop a live LLM session before an unsafe action completes.
- Map AI systems to their underlying identities Identify every API key, service account, plugin credential, and delegated token an LLM can use.
- Test for indirect prompt injection and multi-turn abuse Run scenarios where the malicious instruction arrives through retrieved documents, not just user prompts, and where the attack unfolds over several benign-looking turns.
What's in the full article
Impart's full blog post covers the operational detail this post intentionally leaves for the source:
- Side-by-side control breakdowns for LLM security and runtime protection across prompt, output, and session layers
- Examples of how static policy enforcement differs from inline behavioural blocking in production AI applications
- A practical comparison table that helps teams decide which controls belong in governance, detection, or enforcement
- Discussion of where the vendor sees the category lines moving as access governance and runtime security converge
👉 Read Impart Security's analysis of LLM security vs. LLM protection →
LLM security vs. runtime protection: are your controls keeping up?
Explore further
01/08/2026 10:57 pm
LLM security and LLM protection are not competing labels, they are different control classes. One class governs access, configuration, logging, and known-pattern detection. The other class intervenes during execution when the model is already acting on context. Security teams that collapse the two terms risk overestimating what posture controls can do against live abuse. The governance lesson is simple: classification errors create control gaps, especially where AI systems inherit credentials and permissions from surrounding identity layers.
A question worth separating out:
Q: Who should own the risk when an LLM misuses delegated access?
A: Accountability should sit with the team that approved the model's access, the owners of the underlying identities, and the incident response function that can revoke them. AI governance, IAM, and application teams all have a role, but nobody should assume the model is outside normal access governance.
👉 Read our full editorial: LLM security vs. LLM protection: where runtime control matters
