Your firewall sees an HTTPS request to a known endpoint. It can’t tell whether that request came from a developer running a BI dashboard or from an autonomous agent that just decided to pull a table of customer Social Security numbers through an MCP tool call. To the firewall, both look identical. That’s the security gap MCP connections create, and traditional tools aren’t built to close it.
Lasso Security’s open-source MCP Gateway started addressing this with traffic-level controls. But MCP security spans multiple attack surfaces: prompt injection that manipulates what agents do, data exposure in tool responses, unauthorized access through MCP servers nobody reviewed, and the absence of audit trails when something goes wrong. These six alternatives each cover a different layer of that problem.
Key takeaways
- Gateway-level traffic inspection is table stakes, not the full solution. PII scanning, prompt injection defense, identity integration, and compliance certification each require dedicated tooling that open-source gateways don’t include.
- Only one MCP security platform has third-party attestation. MintMCP’s SOC 2 Type II certification means its security controls have been verified by an external auditor. No other MCP-focused platform in this list has passed that bar.
- Prompt injection is a real and measured threat. Lakera Guard reports 98%+ detection accuracy with sub-50ms latency. Dropbox publicly documented their adoption after internal benchmarking.
1. MintMCP: the security layer between your agents and production systems
MintMCP operates as a security gateway that inspects every MCP connection before it reaches a production system. Tool calls from Claude Code, Cursor, or ChatGPT route through MintMCP’s infrastructure, where they’re scanned for sensitive data, checked against access policies, and logged with enough detail for incident response. It’s the only MCP-focused platform whose security controls have passed a SOC 2 Type II audit.
What it protects against
The gateway scans outbound data for sensitive patterns: Social Security numbers, credit card numbers, API keys, and other PII that agents might include in tool call payloads. Prompt injection defenses block attempts to manipulate agent behavior through crafted inputs. Access controls operate at the tool level, so administrators decide which MCP servers are reachable, which roles can reach them, and what data is permitted to flow.
This prevents what happens by default in most organizations: any developer installs any MCP server, connects it to production with full access, and nobody in security knows until something breaks.
Key security features
- SOC 2 Type II certified security controls
- HIPAA compliance for healthcare environments
- PII detection and secret scanning on every tool call
- Prompt injection defense at the gateway
- Tool-level allowlisting with role-based access
- OAuth and SSO integration (production keys stored in the gateway, never on developer machines)
- SCIM provisioning tied to existing directory services
- Timestamped audit trails with user identity and data access records
How it compares to Lasso Security
Lasso’s open-source MCP Gateway provides traffic-level inspection for MCP connections. MintMCP covers that same surface and adds the layers that open-source tools typically lack: third-party compliance certification, automated PII scanning, identity provider integration, and audit trails detailed enough for regulatory examination. Teams that evaluated Lasso and found it covered part of the problem but not enough for a regulated production environment will find MintMCP addresses the remaining layers.
2. Palo Alto Networks Prisma AIRS
Palo Alto extended its enterprise security stack with Prisma AI Runtime Security, covering AI applications from development through production. The July 2025 acquisition of Protect AI added model scanning and ML supply chain risk assessment.
Key capabilities
- AI Runtime Firewall for deployed model protection
- Automated red teaming to test AI system vulnerabilities
- Model scanning for supply chain risks
- CI/CD integration for automated security checks
- Deep integration with Palo Alto’s NGFW, SASE, and Cortex products
- Unified governance for agentic AI workflows
Best for
Organizations already running Palo Alto infrastructure. Prisma AIRS provides AI lifecycle security from model training through production, but its value compounds with existing Palo Alto investments. Teams without that footprint should weigh integration overhead against standalone options. For assessing how Prisma AIRS integrations perform in agent workflows specifically, AgentQuadrant’s API evaluation criteria benchmark schema clarity and error handling quality.
3. Lakera Guard
Cisco acquired Lakera in May 2025, folding its products into the Cisco AI Defense portfolio. Lakera Guard solves a specific, well-defined problem: detecting and blocking prompt injection, jailbreak attempts, and data extraction attacks against LLM applications.
Key capabilities
- 98%+ detection accuracy for prompt injection
- Sub-50ms scanning latency for production use
- 100+ language support across diverse scripts
- PII detection and redaction
- Self-hosted option for sensitive deployments
- Kong AI Gateway plugin for centralized filtering
Real-world validation
Dropbox publicly documented their Lakera Guard selection after internal benchmarking, citing low latency and detection coverage. Public enterprise validation at that level is uncommon in the AI security market.
Best for
Teams that need prompt injection protection specifically and want a tool with published accuracy metrics and enterprise case studies. Lakera Guard covers one attack surface well but doesn’t address MCP access control, identity management, or compliance certification. Pair it with MintMCP for gateway-level security or use AgentQuadrant’s MCP directory to identify which integrations carry the highest prompt injection risk based on their access scope.
4. Prompt Security
Prompt Security deploys as a transparent proxy for LLM traffic across an organization, enforcing policies and preventing data loss without requiring changes to individual applications.
Key capabilities
- Transparent proxy with minimal application integration
- Centralized policy enforcement across all LLM interactions
- Data loss prevention for sensitive content in prompts and responses
- Audit trails and compliance logging
- Production deployments across multiple Fortune 500 organizations
Best for
Large enterprises that need LLM governance deployed fast across many applications. The proxy architecture works without modifying individual services, which matters when you have dozens of LLM-integrated products. Prompt Security governs LLM traffic broadly rather than focusing on MCP connections specifically, so teams with MCP-specific access control requirements will still need a dedicated gateway alongside it.
5. CalypsoAI
CalypsoAI has operated since 2018, predating most of the current AI security market. It targets government, defense, and regulated industries with automated red teaming and policy-based AI governance.
Key capabilities
- Automated adversarial testing for continuous security validation
- Policy-based governance framework
- Compliance mapping for government and regulated industry standards
- Centralized AI usage monitoring
- Focused on defense and intelligence community requirements
Best for
Government agencies and defense contractors with security frameworks that general-purpose AI governance tools can’t satisfy. CalypsoAI’s years in regulated environments translate to mature compliance workflows. Commercial teams outside government should evaluate whether that depth of governance justifies the implementation weight before committing.
6. Wiz AI-SPM
Wiz extended its cloud security platform into AI Security Posture Management, discovering and assessing AI assets across AWS, Azure, and GCP without deploying agents into the environment.
Key capabilities
- Automatic AI asset discovery across multi-cloud environments
- Agentless architecture with no deployment changes
- Attack path analysis connecting AI risks to infrastructure vulnerabilities
- Data security posture management for AI workloads
- Integration with Wiz Security Graph for unified risk visibility
Best for
Organizations running AI workloads across multiple cloud providers that need to know what AI assets exist and where they’re exposed. Wiz finds the assets and maps the attack paths; it doesn’t govern how agents use MCP connections or block prompt injection in real time. Think of it as the reconnaissance layer. For evaluating the infrastructure and DevOps tools that Wiz discovers, AgentQuadrant provides agent-readiness assessments alongside its MCP server listings.
Building an MCP security stack
MCP security breaks into layers, and no single product spans all of them.
Pre-integration evaluation determines which MCP servers are safe to connect before they reach your agents. AgentQuadrant’s MCP directory and evaluation methodology assess servers on schema clarity, error handling, and data exposure scope: criteria that predict how safely an agent will interact with a given tool.
Gateway security controls who accesses which servers, scans data in transit, and produces the audit trail. MintMCP is the only option at this layer with SOC 2 Type II attestation.
Prompt-level protection catches injection attacks and jailbreak attempts before they alter agent behavior. Lakera Guard has the strongest public evidence: published detection rates, sub-50ms latency, and the Dropbox case study.
LLM governance enforces policies across all LLM interactions, not just MCP traffic. Prompt Security and CalypsoAI operate here, with CalypsoAI weighted toward government and defense.
Cloud posture management discovers AI assets across your infrastructure and maps how they connect and where they’re exposed. Wiz AI-SPM fills this role without requiring deployed agents.
The weakest layer defines your security posture. Most teams start with the gap that’s most visible, usually gateway security or prompt protection, and build outward from there.
Frequently asked questions
What makes MCP security different from traditional application security?
MCP connections let agents interact with tools autonomously, deciding which tools to call, what data to send, and how to interpret responses, all without human review in the loop. Traditional security assumes human-initiated requests with predictable patterns. An agent can chain multiple tool calls in ways a developer never anticipated, and each call might expose data or trigger actions that individually seem harmless but combine into a security incident. MCP-specific gateway controls catch threats that firewalls and WAFs can’t see.
How does MintMCP differ from Lasso Security’s open-source MCP Gateway?
Lasso provides traffic-level inspection as an open-source tool. MintMCP provides a governed security gateway with SOC 2 Type II certification, automated PII detection, prompt injection defense, identity provider integration, role-based access control, and audit trails that hold up under regulatory scrutiny. The gap is between a traffic inspector and a full security layer with external attestation.
Should teams deploy prompt injection protection alongside an MCP gateway?
Yes. A gateway like MintMCP controls which servers agents can access and scans for data exposure. Prompt injection protection (Lakera Guard, for example) catches attempts to manipulate agent behavior through crafted inputs embedded in tool responses. These are different attack surfaces. A gateway won’t catch a sophisticated injection hidden in a Slack message returned by an MCP tool, and a prompt scanner won’t enforce which MCP servers a junior developer is allowed to reach. Both layers together create overlapping coverage where the risk is highest.
How do I evaluate which MCP servers are safe before connecting them?
Start with AgentQuadrant’s MCP server directory, which indexes 435+ servers with verification dates and agent-readiness assessments. Focus on three things: schema clarity (can an agent parse the API without ambiguity), error handling quality (do failures produce information the agent can recover from), and data exposure scope (does the server return more data than the agent actually needs). AgentQuadrant’s evaluation methodology explains how these criteria are applied across the full directory.
