The complete stack for
secure, governed AI agents.

From identity and memory protection to runtime enforcement, EDR attribution, and continuous red-team validation - EngramAI covers every layer of the agentic security stack.

Explore Products
LineageMCP GuardSentinelRedTeamMemGuardAegisYaliEngramAI Platform
Core Idea

One trust layer. Every agent deployment.

AI agents are moving from experimentation to production - accessing databases, executing code, calling APIs, and making decisions. Each capability creates an attack surface. EngramAI provides security and governance across the entire stack.

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The Core Problem

Enterprises deploy AI agents with more autonomy than their security teams can see, control, or attribute. When an agent accesses sensitive data, executes a dangerous command, or is hijacked via prompt injection - the SOC sees a kernel event but cannot trace it back to the agent intent that caused it. Memory stores can be poisoned. MCP tool servers can be compromised. The model itself can be jailbroken.

Zero end-to-end visibility into AI agent actions.
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The EngramAI Approach

Every product answers one layer of the same question: “What is the agent doing, who authorized it, and should it be allowed?” From memory poisoning detection (MemGuard) to identity (agent-identity) to protocol enforcement (MCP Guard) to trajectory enforcement (Aegis) to EDR attribution (Lineage) to continuous validation (RedTeam, Yali).

Identity → Memory → Protocol → Runtime → Kernel → Validation.
Product Portfolio

Seven products. One security stack.

Each product solves a distinct problem in the agentic security landscape. Together they provide end-to-end coverage - from memory and identity to protocol enforcement to SOC attribution to continuous validation.

Lineage
repos/lineage
“Your SOC sees WHAT happened. Lineage tells you WHO caused it.”
AI-attributed Linux EDR overlay that connects kernel/OS events from your existing EDR (CrowdStrike, SentinelOne, Cortex XDR, Defender) with LLM agent intent traces from Langfuse. When a process opens a sensitive file or calls a remote IP, Lineage tells you which agent did it, what the user asked, and whether the intent matched the action. Verdicts are written back into your EDR console as first-class detections.
Go57 moduleseBPFLLM JudgeCrowdStrikeSentinelOneSplunkDockerCI/CD
EDR is $15B+ at 12–15% CAGR. Zero direct competitors in AI agent attribution. Complements (doesn’t replace) existing EDR budget. Every major EDR vendor has AI detection but zero intent attribution.
MCP Guard
repos/mcp_guard
“The active runtime security firewall for the Model Context Protocol.”
A stateful security interceptor between AI hosts (Claude, GPT, Cursor) and MCP tool servers. Inspects and governs every JSON-RPC message in real time using a 2-tier engine: deterministic policy rules at sub-millisecond latency (L1) and Claude 3.5 Haiku intent analysis for semantic threat detection (L2). Blocks prompt injection, privilege escalation, data exfiltration, and confused deputy attacks before they reach tool servers.
Rust17 src modulesJSON-RPC proxyWeb dashboardHomebrewSecurity audit
MCP is the emerging standard for agent-to-tool communication. Every enterprise adopting Claude Desktop, Cursor, or any MCP-enabled tool needs protocol-layer security. No active competitor in this space.
Sentinel
repos/sentinel
“Transparent observability sidecar for Model Context Protocol.”
A passive, non-blocking observability layer between LLM clients and MCP tool servers. Unlike MCP Guard (which blocks), Sentinel observes - providing identity, ordering, and integrity guarantees for agent-to-tool interactions without modifying execution. Reconstructs a canonical event stream with real-time visualization and durable audit logs. Complement to MCP Guard: Sentinel watches, MCP Guard acts.
Rust17 src modulesPassive tapWeb dashboardHomebrew9 stars
SOCs and compliance teams that need audit trails for MCP interactions without blocking agent workflows. “See what your agent did, in what order, and trust the record afterward.”
Aegis
repos/aegis
“Runtime security enforcement for autonomous AI agents.”
A runtime security layer between agent frameworks and the tools they call. Observes the evolving execution trajectory - not just individual tool calls but the chain of actions - to catch dangerous sequences that isolated permission checks miss. Uses live execution graphs and contextual policy to ALLOW, WARN, or BLOCK.
Python SDKpip installExecution graphTrajectory risk
Agent developers and security teams that need trajectory-level enforcement - “send_email() is fine alone, but dangerous when preceded by extract_credentials() after an untrusted webpage input.” Complements MCP Guard (protocol layer) by operating at the agent framework layer.
RedTeam (AIRedTeam)
repos/AIRedTeaming (RedNPurple)
“Drive real attack payloads at production LLMs. Emit SOC-ingestible evidence.”
Production-grade LLM red-team runner executing 44 atomic tests against the models your customers call. Verifies whether the model exfiltrated data, generated harmful code, or leaked the system prompt. Ships results to the SOC in SIEM-native formats: SARIF, STIX 2.1, OSCAL, CEF, LEEF, Splunk HEC. Five independent voters per verdict. HMAC-chained audit log. 150+ tests passing.
Rust44 atomic testsOWASP LLM 10/10MITRE ATLAS9 export formatsSIEM-native
Every regulated enterprise running LLMs in production needs continuous adversarial testing. SOC-2, ISO 42001, EU AI Act compliance requires documented adversarial testing. RedTeam turns red-teaming from a Confluence screenshot into an automated, auditable pipeline.
Yali
repos/Yali
“Autonomous red-teaming harness for agentic AI systems.”
Multi-agent red-teaming harness that discovers vulnerabilities in AI agents, MCP servers, and LLM-powered applications. 39+ attack patterns mapped to OWASP LLM Top 10, OWASP Agentic AI Top 10, and MITRE ATLAS. A Rust orchestrator runs Reconnaissance, Attack, and Exfiltration agents in a coordinated swarm. Delta validation for CI/CD - if the security score drops, the build fails. Unlike RedTeam (which tests individual model responses), Yali tests full agentic systems with multi-step attack campaigns.
Rust39+ patternsMulti-agent swarmCI/CD deltaDocker
Agent developers and security teams needing continuous adversarial testing of full agentic systems - not just individual LLM calls. Complements RedTeam: RedTeam validates models, Yali validates agent deployments.
MemGuard
repos/memguard
“Enterprise-grade memory poisoning detection for RAG systems.”
Protects AI agents from AgentPoison attacks with 100% detection accuracy. The attack: an adversary injects <0.1% poisoned entries into a knowledge base. Those entries look normal in text but cluster tightly in embedding space (>0.95 similarity). When a user query hits a trigger, the poisoned memory is retrieved and the agent executes the attacker’s malicious action. MemGuard’s comparison-based detection catches what text classification (0%) and raw embedding classification (50%) miss.
GoPython SDKEmbedding analysis100% detectionAgentPoison
Every enterprise deploying RAG-based AI agents is vulnerable to memory poisoning. As agents become more autonomous and access larger knowledge bases, the attack surface grows. MemGuard is the only product specifically targeting this vector with published academic validation.
EngramAI Platform
repos/engramai + defense-layer + agent-identity
“Trust layer for agentic AI - identity, governance, and runtime protection.”
The distributed multi-tenant security platform tying everything together. Immutable agent identity via DIDs and verifiable credentials. Agent Provenance Graph (APG) for behavior-aware governance. Memory poisoning detection (MemGuard) propagation risk detection.
Go gatewayAWS/AzureDIDs + VCsBayesian inferenceSupply chain
The unified platform that ties all EngramAI products together - multi-tenant SaaS, distributed governance, and the agent identity backbone for enterprise deployments.
At a Glance

Seven products. One unified platform.

ProductCore IdeaPrimary Buyer
LineageAI-attributed EDR overlay - kernel events attributed to LLM agent intent via EDR + Langfuse joinCISO, SOC Director
MCP GuardActive MCP protocol firewall - sub-ms policy + Claude intent analysis, blocks attacks before tool serversAI Platform, DevSecOps
SentinelPassive MCP observability sidecar - tamper-proof audit trail, identity + ordering + integrity, no blockingCompliance, GRC, SOC
RedTeamAutomated LLM red-team runner - 44 atomic tests, 5-voter verdicts, 9 SIEM export formats, CI/CD-readyCISO, Compliance
MemGuardRAG memory poisoning detection - 100% detection of AgentPoison attacks via comparison-based embedding analysisAI Security, RAG Platform Lead
AegisAgent runtime trajectory enforcement - catches dangerous action chains that isolated permission checks missAgent developers, AI Security
YaliMulti-agent red-teaming harness - 39+ attack patterns against full agentic systems, CI/CD delta validationAgent developers, Red Team
EngramAI PlatformUnified trust layer - DIDs, VCs, APG governance, memory protection, supply chain attestation, multi-tenant SaaSCISO, VP Engineering
Get Started

Every layer of the agentic security stack.

From memory protection and identity to protocol enforcement to SOC attribution to continuous red-team validation - EngramAI has a product for every layer.

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