What should I look for in an AI agent security platform?

Coverage across all three layers where agents run (endpoints, cloud/SaaS, and your own software), inline enforcement rather than alert-only, first-class MCP support, adaptive red teaming that maps to recognized frameworks, and deployment options that keep sensitive data in your environment.

Is prompt filtering enough to secure AI agents?

No. Prompt filtering inspects text, but most agent risk is in the actions an agent takes: which tools and MCP servers it calls, which identities it assumes, and which data it touches. Effective agent security controls those actions at runtime, using prompt analysis as one input among several.

What is the difference between monitoring and enforcement?

Monitoring (alert-only) tells you an agent did something risky after the fact. Enforcement blocks, sanitises, throttles, or terminates the action inline, while it is happening. A good platform supports both: monitor-only to discover and tune, then enforce on the highest-risk paths.

How to evaluate an AI agent security platform

The AI agent security market is new and the words are overloaded. This is a practical, vendor-neutral checklist for comparing platforms, followed by where Wingback fits. Use it to write your own evaluation criteria.

The criteria that matter

Criterion	What to ask
Coverage	Does it secure all three layers where agents run: employee endpoints, cloud and SaaS, and your own software? Or only one?
Enforcement	Can it block a malicious action inline, or does it only alert after the fact? Does it support monitor-only and enforce modes?
Action control	Does it govern tool and MCP calls, identity usage, and data access, or only inspect prompt text?
MCP support	Is the Model Context Protocol a first-class citizen: gateway, credential isolation, tool scanning, and posture checks?
Red teaming	Does it include adaptive, continuous adversarial testing that maps findings to the OWASP LLM/Agentic/MCP Top 10 and MITRE ATLAS?
Frameworks	Does it produce audit evidence for ISO 42001, NIST AI RMF, and the EU AI Act?
Deployment & data	Can it run as SaaS, dedicated, or self-hosted in your VPC? Where do sensitive payloads live?
Integration effort	Does it deploy as a proxy, sidecar, or SDK without rewriting your agents? How fast is time-to-value?

Approaches, compared

Most AI agent security today falls into one of three approaches:

Prompt filtering only. Inspects input and output text. Useful, but blind to the actions an agent takes, which is where most real risk lives.
Posture and discovery only. Inventories AI assets and flags misconfigurations. Valuable for visibility, but does not stop anything at runtime.
Agent Detection & Response. Combines discovery with inline control of agent actions across every layer. This is the category Wingback is built for.

Where Wingback fits

Wingback is an Agent Detection & Response platform that covers all three layers, enforces inline, controls agent actions rather than only text, treats MCP as first-class, includes adaptive red teaming, and deploys as SaaS, dedicated, or self-hosted. It is delivered forward-deployed: our engineers embed with your team and wire it to your exact stack.