By now you understand the potential threats of agentic AI. Prompt injection, memory poisoning, tool misuse, privilege escalation, shadow agents. The attack surface is documented and the regulatory pressure is building. What you need is a way to evaluate the platforms that claim to solve these problems and pick one that actually works for your environment.
Here is the uncomfortable truth: most of the platforms in this space are new. The feature matrices look similar on every website. Every vendor says they do runtime enforcement, shadow discovery, and SIEM integration. But when you run a POC, the differences emerge fast and they matter a lot.
This guide profiles the agentic AI security solutions that matter, explains the architecture decisions that separate real platforms from marketing pages, and gives you a framework for running an evaluation that actually tells you something.
The current solutions landscape
The market splits into two categories, and the distinction matters for how you buy.
Platform-native solutions live inside an existing security ecosystem. Cortex AgentiX runs inside Palo Alto Networks’ Cortex stack. SentinelOne Purple AI runs inside the Singularity platform. Vectra AI extends its behavioral detection platform to cover agent activity. If your organization is already invested in one of these ecosystems, the integration argument is compelling: agent telemetry appears in the same console as endpoint, network, and cloud data, and enforcement actions execute through workflows your team already uses.
Standalone solutions sit across heterogeneous environments. Prompt Security operates as an MCP gateway layer between your AI applications and the tools they connect to. Zenity provides agent governance across SaaS, cloud, and endpoint from a single console. Salt Security maps the full agent-to-API relationship graph. Reco AI focuses on SaaS-embedded AI discovery and governance. Prophet Security targets the SOC alert triage use case specifically. BeyondTrust extends privileged identity management to AI agents.
The platform-native vendors argue that fragmented tooling creates the visibility gaps attackers exploit. The standalone vendors argue that AI agent security is too different from traditional security to bolt onto an XDR. Both are right in different contexts. Which context is yours is what the evaluation section of this guide is for.
Platform profiles
Cortex AgentiX (Palo Alto Networks)
Persona-based system agents automate investigation lifecycles across email threats, endpoint forensics, threat intelligence enrichment, and cloud security posture. Analysts prompt agents in natural language. The platform generates execution plans that sequence actions across integrated tools. What distinguishes it from every other platform on this list is governed workflow autonomy: agents plan and execute inside enterprise guardrails with role-based access control (RBAC), human-in-the-loop approvals, tool allowlists, and policy-based enforcement at execution time. Full audit trails capture every action.
Integrates with: Cortex XSIAM, Cortex XSOAR, Cortex XDR, Cortex Cloud, third-party security tools via playbooks. If you are not already in the Cortex ecosystem, the integration depth argument is weaker. If you are, agents operate with the same telemetry and enforcement surface as your existing SOC workflows.
Prompt Security
Operates as a runtime enforcement layer sitting between AI applications and the tools, models, and data sources they connect to. Designed for organizations dealing with AI tool sprawl who need visibility without rearchitecting their stack. The standout capability is MCP gateway security with dynamic risk scoring across over 13,000 known MCP servers. Prompt injection detection happens at execution time. Sensitive data redaction happens at the egress layer. Shadow AI discovery runs continuously.
Prompt Security is the only vendor on this list whose core architecture is built around MCP-level control. If MCP adoption is accelerating in your environment and you need protocol-level inspection rather than API-level monitoring, this is the category to watch.
Zenity
Covers the full agent lifecycle from discovery through runtime threat response, with unified visibility across SaaS-managed agents, cloud-deployed platforms, and endpoint-based agents from a single console. The Correlation Agent is the differentiator: it analyzes complete execution paths (tool calls, memory access, data sequences, control flow) to surface malicious intent that individual API-level actions would mask. This is intent-focused detection in practice, not just on a slide deck. Dynamic graph analysis stitches build-time configuration with runtime behavior to expose how seemingly isolated misconfigurations compound into exploitable attack chains.
Prophet Security
Transforms SOC operations by autonomously triaging, investigating, and responding to alerts at machine speed. Full alert investigations complete in under three minutes with dynamic reasoning that mirrors how expert analysts approach complex security events. Less of an agent security platform and more of an agentic SOC analyst. If your primary pain point is alert volume overwhelming analyst capacity rather than securing specific AI agent deployments, Prophet addresses a different but overlapping problem.
Reco AI
Secures agentic workflows through dynamic SaaS security, combining AI agent discovery with real-time behavioral monitoring. The Knowledge Graph correlates security events across disparate SaaS applications, identity systems, and AI tool deployments. Best for organizations with SaaS-heavy environments where AI features are embedded in productivity and business tools and shadow AI is the primary concern. Non-human identity governance (OAuth token sprawl, service account permissions) is where Reco differentiates.
Vectra AI
Delivers behavioral detection against adversaries who weaponize AI to accelerate attack campaigns across hybrid infrastructure. AI Stitching correlates attack behaviors across network, identity, and cloud domains to construct complete attack narratives from fragmented signals. The key capability for agent security specifically: detecting when legitimate AI agents are abused for reconnaissance or lateral movement, something API-level logging alone cannot surface.
SentinelOne Purple AI
Operates as an agentic security analyst combining deep reasoning with autonomous investigation and response. Auto-investigate conducts end-to-end investigations spanning discovery, alert assessment, hypothesis validation, and impact analysis, with every step documented for human approval before action. Best for security teams that want autonomous investigation without analyst bottlenecks. The integration requirement is a consideration: Purple AI delivers its strongest results on the Singularity platform.
Other platforms worth knowing
Salt Security takes an API-centric approach, using an agentic security graph to map every agent, MCP server, and API connection. BeyondTrust extends privileged identity management to AI agents, treating them as identities that need the same governance as human and machine accounts. Cisco integrates agentic AI security into its Zero Trust architecture through an MCP gateway.
The architecture decision that matters
When you evaluate any of these platforms, there is one architectural question that separates real enforcement from sophisticated monitoring.
Does the platform enforce controls externally and deterministically, or does it rely on the agent’s own reasoning to stay within bounds?
This matters because LLMs are probabilistic reasoning engines, not security enforcement mechanisms. You can instruct an agent to refuse certain requests. Prompt injection techniques override those instructions. You can tell an agent to respect access boundaries. It has no reliable mechanism to enforce them. Attempting to constrain agent behavior through prompting or alignment runs against the fundamental value proposition of agents, which is their ability to adapt dynamically.
AWS, in their response to the NIST CAISI Request for Information on securing AI agent systems, articulated this as the “security box”: deterministic, infrastructure-level controls that sit outside the agent’s reasoning loop. Every interaction between the agent and the outside world passes through them. Model manipulation cannot bypass these controls because the controls do not live inside the model.
How do these platforms actually keep AI agents from doing things they should not?
Prompt Security implements this at the MCP protocol layer. Every interaction between an AI application and an MCP server is inspected, risk-scored, and enforced before reaching downstream systems. The enforcement is external to the agent.
Zenity implements this through inline prevention at execution time. Tool calls are intercepted and validated against approved workflows before the action executes. The Correlation Agent analyzes intent, but the enforcement is deterministic.
Cortex AgentiX implements this through policy-based enforcement at execution time with tool allowlists and RBAC. The agent plans. The platform enforces.
The platforms that do not do this, the ones that rely on output filtering or prompt-level guardrails, are the ones where POC results will look good and production results will not.
Intent-focused detection versus API-level logging
Knowing which API an agent called tells you almost nothing. Understanding why it called it and whether the outcome aligns with its authorized purpose is where meaningful detection lives.
This is the test to run in every POC: simulate an agent retrieving data through a legitimately authorized API for an unauthorized purpose. An agent that has database access accesses a database. No rule fires. No alert triggers. The question is whether the platform flags the behavioral mismatch, the gap between what the agent was supposed to do and what it actually accomplished.
Zenity and Vectra AI are the two platforms that compete most directly on this capability. Zenity’s Correlation Agent analyzes complete execution paths. Vectra’s AI Stitching correlates across network, identity, and cloud. Both answer “what was the agent trying to accomplish” rather than “which API was called.”
The maturity model as an evaluation lens
Organizations do not go from zero to full autonomy in a single quarter. Security programs evolve through four stages, and different platforms serve different stages better.
Stage 1: Observe. You need to know what agents exist and what they are doing. Continuous discovery across SaaS, cloud, and endpoint is the priority. Reco AI and Salt Security are strong at this stage because their discovery engines are purpose-built for SaaS and API environments respectively.
Stage 2: Govern. Policies are defined. Agents are bound to approved tool sets, access scopes, and behavioral baselines. RBAC is enforced. Human-in-the-loop approvals are in place for high-impact actions. Zenity, BeyondTrust, and Cortex AgentiX are built for this stage.
Stage 3: Enforce. Policies execute in real time. Prompt injection attempts are blocked before execution. Unauthorized tool calls are intercepted. Data redaction happens at the egress layer. Prompt Security and Zenity compete most directly here, with Prompt Security focused on MCP-level enforcement and Zenity on full-lifecycle inline prevention.
Stage 4: Autonomous Response. The platform detects deviations, correlates context, and initiates response actions automatically. Cortex AgentiX, SentinelOne Purple AI, and Prophet Security are strongest here because they already operate as autonomous SOC analysts with response playbooks.
Most organizations in 2026 sit somewhere between Observe and Govern. The platforms closing the gap fastest toward enforcement are the ones building toward Stage 3 and 4 by default, not as premium add-ons.
How to evaluate solutions: the capability checklist
Not all capabilities matter equally. Some are table stakes. Some are differentiators. Some are proxies for architectural decisions you cannot see on a feature matrix.
Minimum viable controls
Before scoring any vendor on advanced capabilities, confirm these five baseline controls. If any are missing, the platform is not ready for enterprise deployment.
RBAC with role-based permission boundaries. Agents should operate within the same permission model as human analysts. If the platform cannot enforce this, every agent running on it is an insider threat waiting to happen.
Human-in-the-loop approvals for high-impact actions. Not for every action. Requiring human approval for everything is a failure mode: the volume overwhelms reviewers and approval becomes reflexive. But actions that modify production data, initiate financial transactions, or communicate sensitive information externally must have a human gate.
Immutable audit logs with full context. Every agent action must be logged with who invoked the agent, what actions were taken, which systems were accessed, which data were touched, and why. Tamper-proof. Incomplete logs undermine both compliance and incident investigation.
Tool allowlisting with execution-time enforcement. Agents are bound to approved tool sets. Invocations outside scope are intercepted before execution. Not flagged after. Intercepted before.
Continuous agent discovery across all environments. SaaS platforms. Cloud workloads. Endpoint deployments. Sanctioned and shadow deployments. If the platform cannot answer “how many AI agents are running in my environment right now,” it cannot govern what it cannot see.
Differentiator capabilities
These differentiators separate mature platforms that secure AI agents from feature-checkbox products.
Intent-focused detection. The platform should flag behavioral mismatches, not just API calls. Test this by simulating an agent retrieving data through a legitimately authorized tool for an unauthorized purpose.
Indirect prompt injection detection. Most platforms can scan user prompts for direct injection patterns. Fewer inspect content that agents retrieve from documents, emails, and web pages. This is where real attacks happen. Test it in your POC.
Memory integrity monitoring. Corrupted or tampered agent memory should be detected before it influences downstream decisions. This capability is rare and underappreciated.
Execution-time policy enforcement. Post-execution analysis is forensics. By the time an alert fires, the damage may already be complete. Enforcement must happen before actions execute.
Bidirectional SIEM/SOAR/XDR integration. Ingest-only integrations mean your SIEM can see what happened but cannot trigger containment actions. Test whether your SOAR can execute agent isolation playbooks through the platform’s API.
Agentic AI security platform capability comparison
Lifecycle coverage
Which stages of the agent lifecycle each platform addresses.
| Vendor | Build-time posture | Runtime enforcement | Post-incident response |
|---|---|---|---|
| Palo Alto Cortex AgentiX | Yes | Yes | Yes |
| Prompt Security | Yes | Yes | No |
| Zenity | Yes | Yes | Yes |
| Prophet Security | No | Yes | Yes |
| Reco AI | Yes | Yes | No |
| Vectra AI | No | Yes | Yes |
| SentinelOne Purple AI | No | Yes | Yes |
| Salt Security | Yes | Yes | No |
| BeyondTrust | Yes | Yes | No |
Discovery scope
Where each platform can find agents, including those deployed without security review.
| Vendor | SaaS platforms | Cloud workloads | Endpoint | Shadow AI detection |
|---|---|---|---|---|
| Palo Alto Cortex AgentiX | Yes | Yes | Yes | Via integrations |
| Prompt Security | Yes | Yes | Yes | Yes |
| Zenity | Yes | Yes | Yes | Yes |
| Prophet Security | Via SIEM | Yes | No | No |
| Reco AI | Yes | Yes | No | Yes |
| Vectra AI | Yes | Yes | Yes | Via behavioral |
| SentinelOne Purple AI | Yes | Yes | Yes | Via integrations |
| Salt Security | Yes | Yes | No | Yes |
| BeyondTrust | Via IdP | No | No | Via IdP |
Control depth
Governance mechanisms each platform enforces, not just reports on.
| Vendor | RBAC | Human-in-the-loop | Tool allowlisting | Data redaction | Audit logs |
|---|---|---|---|---|---|
| Palo Alto Cortex AgentiX | Yes | Yes | Yes | Yes | Yes |
| Prompt Security | No | No | Yes | Yes | Yes |
| Zenity | Yes | Yes | Yes | No | Yes |
| Prophet Security | No | Yes | No | No | Yes |
| Reco AI | Yes | No | No | Yes | Yes |
| Vectra AI | Yes | No | No | No | Yes |
| SentinelOne Purple AI | Yes | Yes | No | No | Yes |
| Salt Security | No | No | Yes | No | Yes |
| BeyondTrust | Yes | Yes | No | No | Yes |
Detection methodology
How each platform identifies threats, from rule-based matching to intent analysis.
| Vendor | Detection approach | What it catches that others miss |
|---|---|---|
| Palo Alto Cortex AgentiX | Policy-based + workflow analysis | Agent actions that violate defined guardrails |
| Prompt Security | Protocol-level inspection | Malicious MCP interactions before they reach tools |
| Zenity | Intent-focused graph analysis | Legitimate API calls used for illegitimate purposes |
| Prophet Security | Dynamic reasoning | Alert patterns across disconnected data sources |
| Reco AI | Behavioral + relationship graphing | Risky inter-app permissions and token misuse |
| Vectra AI | Behavioral AI + signal stitching | Agents abused for lateral movement or recon |
| SentinelOne Purple AI | Autonomous investigation | Attack chains spanning endpoint, identity, and cloud |
| Salt Security | API relationship mapping | Agent-to-API paths that cross security boundaries |
| BeyondTrust | Identity-driven policy | Over-privileged agent identities and credential sprawl |
Integration architecture
What each platform connects to natively, without custom development work.
| Vendor | SIEM | SOAR | XDR | IdP | MCP security |
|---|---|---|---|---|---|
| Palo Alto Cortex AgentiX | Native | Native | Native | Native | Native |
| Prompt Security | Yes | Yes | No | No | Native |
| Zenity | Yes | Yes | Yes | Yes | No |
| Prophet Security | Yes | Yes | No | No | No |
| Reco AI | Yes | Yes | No | Yes | No |
| Vectra AI | Yes | Yes | Yes | Yes | No |
| SentinelOne Purple AI | Yes | Yes | Yes | No | Yes |
| Salt Security | Yes | Yes | No | No | No |
| BeyondTrust | Yes | No | No | Native | No |
Best-fit scenario
Quick-reference: which organizational profile maps to which platform.
| Vendor | Best fit |
|---|---|
| Palo Alto Cortex AgentiX | Large enterprises already in the Cortex ecosystem needing platform-native SOC workflows |
| Prompt Security | Organizations with AI tool sprawl and high MCP adoption needing protocol-level control |
| Zenity | Enterprise security teams governing complex, multi-environment agent ecosystems |
| Prophet Security | SOC teams drowning in alerts who need autonomous triage without adding headcount |
| Reco AI | SaaS-heavy organizations where shadow AI embedded in business tools is the primary risk |
| Vectra AI | Hybrid infrastructure environments defending against AI-powered adversaries |
| SentinelOne Purple AI | Security teams wanting end-to-end autonomous investigation and response |
| Salt Security | API-first organizations that need to map and secure every agent-to-API connection |
| BeyondTrust | Organizations where agent identity governance and credential management is the gap |
Running a POC that tells you something
Most POCs in this space produce false confidence. The vendor sets up a demo environment. The test scenarios are designed to show strengths. Nobody tests the edge cases that matter.
Here is a POC framework designed to surface real differences.
Week 1: Discovery
Deploy the platform against a representative environment that includes at least one SaaS platform, one cloud workload, and one endpoint deployment. Do not tell the vendor what agents exist. Ask them to show you the inventory.
Pass criteria: All known agents surface within the discovery window. Shadow deployments are flagged automatically. The inventory includes agents you did not know about.
Red flag: The platform only discovers agents deployed through its own SDK or agent. This is not discovery. This is registration.
Week 2: Direct threats
Test direct prompt injection. Submit a prompt designed to redirect the agent’s behavior to an unauthorized action. Test indirect prompt injection: embed a malicious instruction inside a document the agent retrieves. Test privilege escalation: attempt to invoke a tool or access a resource outside the agent’s assigned role.
Pass criteria: All three attempts are blocked at runtime before execution. The platform logs the attempt with full context (who, what, which system, what was attempted).
Red flag: Direct injection is detected but indirect injection is not. Platform flags the action after it executes instead of blocking it. Attempt is logged as a generic API call without context.
Week 3: Behavioral detection
Simulate an agent retrieving data through a legitimately authorized API for an unauthorized purpose. The API call itself looks normal. The question is whether the platform detects the behavioral mismatch.
Pass criteria: Platform flags the discrepancy between the agent’s authorized purpose and what it actually accomplished. The alert includes context about execution path, not just the API endpoint.
Red flag: No alert fires. Or an alert fires for “unusual API usage” without explaining why the usage was unusual.
Week 4: Memory and persistence
Attempt to write corrupted or adversarial data to the agent’s memory. Test whether poisoned memory persists across sessions and influences downstream decisions.
Pass criteria: Platform detects and flags tampered memory before it influences downstream decisions. Memory integrity checks run automatically.
Red flag: No memory monitoring capability exists. This is true of most platforms today. It should factor into your risk assessment.
Week 5: Integration and response
Test bidirectional connectivity with your existing stack. Verify telemetry flows into your SIEM. Trigger a SOAR playbook that isolates a compromised agent through the platform. Test that audit logs capture end-to-end agent sessions with complete context.
Pass criteria: SIEM ingests agent telemetry alongside endpoint and cloud data. SOAR executes containment actions through platform API. Audit logs capture who invoked the agent, what actions were taken, which systems were accessed, which data were touched, and why.
Red flag: Integration is ingest-only (SIEM can see, cannot act). Audit logs are incomplete: actions are logged but invocation context is missing.
Choosing the right type of agentic AI security solution
The best platform category for your organization depends on three variables: your existing security stack investment, your agent deployment patterns, and your maturity stage.
If you are already invested in a major security platform
Cortex AgentiX, SentinelOne Purple AI, or Vectra AI will integrate more deeply with your existing telemetry and enforcement surface. The tradeoff is coverage scope. These platforms secure agents best inside their own ecosystem. Agents running in SaaS platforms or custom environments outside that ecosystem get thinner coverage or require separate tooling.
If your agents are deployed across heterogeneous environments
Prompt Security, Zenity, or Salt Security offer broader coverage across SaaS, cloud, and custom deployments. The tradeoff is integration depth. You get consistent enforcement across environments but less native correlation with endpoint and network data. Plan for SIEM integration work.
If shadow AI is your primary concern
Reco AI or Salt Security are purpose-built for discovering and governing AI features embedded in SaaS tools. The threat model here is different: you are not securing agents you deployed. You are discovering agents your teams deployed without telling you.
If SOC alert volume is your bottleneck
Prophet Security or SentinelOne Purple AI address the capacity problem more directly than the agent security problem. The value is autonomous investigation at machine speed. The agent-specific security controls are a secondary benefit.
Budget considerations
Most vendors in this space do not publish pricing. The market is too new and deals are negotiated case by case. A few directional signals:
Platform-native solutions are typically priced as add-ons to the existing platform subscription. The incremental cost is moderate if you are already paying for the platform. If you are not, the total cost includes the platform adoption cost.
Standalone solutions are priced per agent, per environment, or per seat. Per-agent pricing gets expensive fast if you have hundreds or thousands of agents. Per-seat pricing is more predictable but may not scale to large deployments.
Open-source alternatives exist through the OWASP Agentic Security Initiative and individual projects on GitHub, but they require in-house integration work and lack the detection models that commercial platforms have built on production telemetry.
The 90-day evaluation to deployment path
Day 1-30: Discover. Run a discovery tool across your environment. The number of agents you find will almost certainly exceed the number you expect. Do not skip this step. You cannot secure what you cannot see.
Day 31-60: Define. Map your agents to the maturity model. Which agents are in Observe? Which need to be in Govern or Enforce? Pick one high-risk agent workflow, a customer-facing agent that queries a database, an internal agent that modifies configurations, and scope your evaluation to that single workflow.
Day 61-90: Test and decide. Run the POC framework against your top two candidate platforms using the single workflow you scoped. The results will tell you more than any vendor demo. Expand from there.
The maturity model is not aspirational. Every organization running agents in production is somewhere on it. The gap between where you are and where you need to be is measurable. Close it incrementally, starting with the agents that have the largest blast radius.