Features to Look for in AI Agent Security platforms
AI agents now plan, call tools, and act across systems. That power expands your attack surface. This guide explains what “AI agent security platforms” should actually do, then details the features buyers should require and how to test them. We align criteria to OWASP Agentic AI, OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, and MCP so security and platform teams can evaluate on shared ground.
What “AI agent security platform” means in 2025
An AI agent security platform protects autonomous or semi autonomous workflows where an LLM plans tasks, selects tools, and executes actions through protocols like MCP. It must cover planning, tool calls, data access, and responses, with controls that are explainable and auditable.
To keep evaluation objective, tie scope to community frameworks. Use OWASP Agentic AI for agent specific threats, OWASP LLM Top 10 for input and output risks, MITRE ATLAS for adversary techniques, and NIST AI RMF for governance and response. These provide a shared vocabulary for buyers and vendors, and help map features to real risks instead of marketing claims.
In scope
Goal and plan mediation before execution
Tool permissioning and least privilege
Input and output protections against injection and leakage
Full traceability for audits and incident response
Evaluation and red teaming tied to recognized threat catalogs
The core threat model buyers should use
Ground requirements in risks that appear in production. Start with three families of failure that recur across incidents and test reports.
Prompt and tool injection paths
Attackers craft inputs or context that steer an agent to reveal secrets or execute unsafe tools. This includes classic prompt injection, retrieval poisoning, and tool parameter manipulation that flips actions from safe to harmful. Tie controls to OWASP LLM01 Prompt Injection and related patterns so detection and blocking are testable.
What to verify
Structural policies that validate instructions, goals, and tool arguments before use
Isolation of external content used for planning or RAG so untrusted text cannot act as code
Clear block reasons and evidence when a step is stopped
Data leakage and memory misuse
Agents mix long term memory, session context, and tool outputs. Without boundaries, they exfiltrate secrets or regulated data. Controls should pair PII and secret detection with response shaping and configurable redaction, then log the decision for audit. Reference OWASP guidance for output handling.
What to verify
Inline masking for PII, secrets, and sensitive identifiers
Context window partitioning so private memory is not reused across tenants
Post response scanning with auto redaction and policy explanations
Objective drift, tool abuse, and escalation
Multi step plans can wander. An agent might chain tools in unexpected ways, escalate privileges, or execute repeated retries that amplify risk. Use MITRE ATLAS to model technique chains and require stop conditions, rate controls, and safe modes that pause or roll back.
What to verify
Bounded plans with maximum steps, time, and monetary budget
Per tool scopes, just in time tokens, and revocation after use
Kill switch, quarantine, and forensic capture for later review
Non-negotiable features to require at runtime
These are the capabilities that separate a policy slide from an operational control. Each item includes buyer tests you can run during a proof of value.
Agent firewall and plan mediation
Intercept goals, plans, and tool calls before execution. The platform should parse a plan into steps, validate preconditions, and block or rewrite unsafe actions. For MCP based stacks, confirm the policy engine can understand MCP messages and return actionable denials. Align rules to OWASP agent risks to keep coverage explainable.
Buyer tests
Submit a plan that includes untrusted web content and verify that tool use is sandboxed or denied
Force a step to request a forbidden domain or file type and check for a deterministic block with a human readable reason
Introduce conflicting goals and verify de escalation to a safe default
Tool permissioning and least privilege
Treat every tool as a sensitive API. Enforce per tool scopes, user and agent identity separation, time boxed grants, and argument level policies. Map identity, access, and audit to NIST AI RMF governance and incident response outcomes.
Buyer tests
Grant a one time scope to an external API and verify automatic revocation after execution
Send an argument that exceeds a policy threshold and confirm a block with evidence
Rotate tool credentials and confirm the agent cannot reuse stale tokens
Cross agent policy engine
Large programs run many agents across frameworks. You need central policies that apply across MCP servers, orchestration layers, and model providers. Require policy authoring once, distribution everywhere, and consolidated logs. Confirm compatibility with the Model Context Protocol so enforcement travels with your architecture.
Buyer tests
Publish a rule once and verify enforcement across two frameworks and two model vendors
Simulate a tenant rule conflict and check resolution order is explicit and logged
Disable a policy and confirm the change propagates within your defined SLA
Protection features for inputs and outputs
Your platform should catch hostile inputs, protect sensitive context, and shape responses without breaking useful tasks. Treat these controls like a policy layer you can test and audit.
Prompt injection detection and structural policies
Require policies that validate intent, strip or neutralize untrusted instructions, and separate user goals from system rules. Mappings to OWASP LLM01 Prompt Injection make coverage measurable and comparable across vendors. Expect explainable blocks with evidence you can export to your SIEM.
Buyer tests
Paste a known injection string inside a PDF or web snippet and verify plan mediation prevents tool misuse.
Poison RAG context with out of scope instructions and check the action is denied with a clear reason tied to policy.
Data loss prevention for PII and secrets
Ask for inline redaction, named-entity detection for regulated data, and secret scanning across prompts, memory, tools, and outputs. Require per tenant context isolation and proof that private memory is not reused. Align outcomes with your privacy policies and audit needs. OWASP guidance on output handling provides a baseline for controls.
Buyer tests
Inject a fake secret in context and confirm masking before model calls.
Attempt cross tenant recall of a prior conversation and verify an empty result with a logged denial.
Content and action guardrails with explainable blocks
Guardrails should enforce what the agent may say or do. Prefer deterministic rules backed by model assisted classifiers, not opaque heuristics. Each block should return the violated rule, the step that triggered it, and the recommended remediation. Tie policy categories to OWASP risks so teams can review coverage by threat.
Observability and incident response you can operationalize
You cannot defend what you cannot see. Demand full traceability and response workflows your SOC already understands.
Full trace of plans, tools, contexts, and outcomes
Logs should capture goals, intermediate thoughts or plans, tool arguments, responses, data sources, and policy decisions. You need searchable timelines and immutable evidence for audits. NIST AI RMF calls out governance, measurement, and management outcomes that benefit directly from this traceability.
Buyer tests
Reconstruct a multi step incident from logs alone and export the trace.
Prove that redactions and denials are recorded with the rule ID and actor.
SIEM and SOC-ready alerts and playbooks
Insist on near real time alerts to your SIEM with normalized fields for rule ID, tool, tenant, user, and severity. Ask for starter playbooks aligned to common threats so analysts can triage quickly.
Buyer tests
Trigger a policy violation and confirm an alert arrives with all required fields in your SIEM within your SLA.
Map alerts to a playbook step by step without vendor consoles.
Kill switch, safe mode, rollback, and forensics
In live incidents, you need to pause or downgrade autonomy, revoke tool tokens, and collect artifacts. NIST AI RMF emphasizes manage and respond functions that these controls enable.
Buyer tests
Press a global kill switch and verify that all agent executions halt and tool credentials are revoked.
Roll back to a last known safe policy bundle and restore service.
Evaluation, red teaming, and pre prod gates
Security posture improves only when testing is continuous and threat informed.
Automated adversarial tests linked to OWASP and ATLAS
Ask for test suites that reference OWASP LLM risks and MITRE ATLAS techniques. This lets you baseline performance and focus on threats that mirror real adversary behavior. Favor tests that include tool misuse, retrieval poisoning, and escalation chains.
Buyer tests
Run a canned suite and require coverage metrics by risk and technique.
Compare two policy versions and confirm an objective improvement index.
Release gates with pass or fail thresholds
Pre prod gates should enforce minimum scores for injection resistance, DLP effectiveness, and action guardrail accuracy. Fail the build if thresholds are not met and publish a report.
Buyer tests
Set a target pass rate for LLM01 scenarios and verify the pipeline blocks releases below the bar.
Continuous validation in CI/CD and MCP aware tests
Tie evaluations to each change in prompts, tools, models, or policies. If you use the Model Context Protocol, require test harnesses that operate on MCP messages and tool schemas so issues appear before agents reach production.
Platform architecture and deployment fit
Controls must fit your stack without breaking latency budgets or vendor choices.
Multi model support and router compatibility
Expect support for hosted APIs and on premises models routed by your gateway. Platforms should treat models as interchangeable targets and enforce policies above the model layer.
On prem, VPC, and air gapped options
Many buyers need private deployments with strict egress control. Ask for data flow diagrams, data residency options, and a no training pledge for your content.
Performance budgets and cost controls
Require clear latency overhead targets for plan mediation and policy checks, plus rate limiting and cost guardrails by tenant. If your agents use MCP, verify compatibility with the current spec and its security model so mediation works across frameworks.
Buyer tests
Measure p50 and p95 latency impact with policy on and off.
Throttle a noisy tenant and confirm graceful degradation rather than failure.
Compliance, assurance, and vendor viability
Security platforms should help you meet internal controls and external expectations.
Mapping to NIST AI RMF and audit evidence
Ask vendors to show policy and logging features mapped to NIST AI RMF outcomes in Govern, Map, Measure, and Manage, and to the Generative AI Profile where relevant. Evidence should include reports, control IDs, and export formats your auditors accept.
Data residency, privacy, and certifications
Request documentation for data handling, retention, residency, and subprocessors. Ask for current certifications or independent assessments and the cadence of third party reviews.
Roadmap transparency and open standards alignment
Favor vendors who align with OWASP work, publish test coverage against ATLAS techniques, and support open interfaces like MCP. This reduces lock in and improves interoperability.
The buyer’s checklist
Use this quick screen during demos and proofs of value.
Runtime
Plan mediation blocks unsafe steps with explainable reasons.
Per tool scopes, time boxed tokens, and argument policies enforced.
Cross agent policy engine applies rules across frameworks and models.
Inputs and outputs
LLM01 style injections are detected and neutralized.
PII and secrets are masked before and after model calls.
Response shaping enforces allowed actions and language patterns.
Observability and response
Full trace of plans, tool calls, and policy decisions exported to SIEM.
Kill switch, safe mode, and rollback available to operators.
Forensics bundles preserve artifacts for audits.
Evaluation
OWASP and ATLAS linked tests with measurable coverage.
Release gates with pass or fail thresholds in CI/CD.
MCP aware tests validate tool schemas and messages.
Architecture and compliance
Multi model, on prem or VPC ready, with published latency budgets.
NIST AI RMF mapping and auditor friendly evidence.
Conclusion
AI agent security platforms should not be black boxes. You need policy controls that are testable, logs your SOC can trust, and evaluations grounded in public frameworks. Use the checklist to standardize demos and proofs of value. Hold vendors to published mappings against OWASP, MITRE ATLAS, NIST AI RMF, and MCP so your investment scales with your program.
