Agentic Red Team & Compliance Platform for AI Deployments

The Problem

Organizations are deploying autonomous AI agents into critical infrastructure through agentic AI orchestration platforms, frameworks like LangGraph, CrewAI, AutoGen, Amazon Bedrock Agents, and enterprise-internal agent builders. These agents don't just generate text, they act. They auto-remediate IT incidents, modify network configurations, deploy infrastructure, triage security alerts, and coordinate across multi-agent workflows. Low-code agent design tools enable non-technical users to build and deploy these workflows, dramatically accelerating adoption.

This is a fundamentally different security challenge than securing traditional AI applications.

What Makes Agentic AI Different

A traditional LLM application takes a prompt, generates a response, and a human decides what to do with it. The blast radius of a failure is a bad answer.

An agentic AI system takes a goal, formulates a plan, selects tools, executes actions, observes results, and iterates, autonomously. The blast radius of a failure is an autonomous system taking unauthorized actions on production infrastructure.

The attack surface expands across four layers that don't exist in traditional AI:

1. The Tool Layer Agents invoke tools, kubectl, database queries, ticketing APIs, deployment pipelines, network configuration endpoints. Each tool carries its own permissions and blast radius. A compromised or manipulated agent doesn't just produce bad text, it executes real operations on real systems. The security question isn't "what did the model say?", it's "what did the agent do, and was it authorized to do it?"

2. The Workflow Layer Agent workflows are state machines, sequences of decisions and actions with branching logic. Can steps be skipped? Can the workflow be driven into an unintended state? Can a carefully crafted input cause the agent to take a legitimate tool call at the wrong point in the workflow, producing an outcome the workflow designer never intended? These are logic vulnerabilities, invisible at the prompt layer.

3. The Delegation Layer Multi-agent systems involve agents passing context and control to other agents. Agent A triages an incident and hands it to Agent B for remediation. What trust assumptions does Agent B make about Agent A's output? If Agent A's context is corrupted, through memory poisoning, adversarial input, or a compromised upstream data source, Agent B inherits that corruption and acts on it with its own tool permissions.

4. The Permission Layer Who authorized this agent to take this action? Is the authorization scoped to the minimum necessary? When a non-technical user designs a workflow and grants it tool access, do they understand the blast radius of what they've authorized? Is there a mechanism to verify that the agent's actual behavior stays within its authorized scope?

What Current Solutions Cover

AI runtime security tools, LLM firewalls and guardrail platforms, operate at the prompt and response layer. They inspect what goes into and out of the LLM, catching prompt injection, jailbreak attempts, PII leakage, and policy violations at the model boundary. This is a necessary and valuable layer of defense.

But the model boundary is one boundary. The agent's tool invocations, workflow state transitions, cross-agent delegations, and permission-scoped actions all happen outside the LLM call. An LLM firewall sees the prompt. It doesn't see the agent picking up a kubectl tool and modifying a production firewall rule, or an incident triage agent silently misrouting a critical alert because its workflow was driven into an unintended state.

The Gap

There is currently no platform that provides:

• Pre-deployment security assessment of agent workflows before they reach production
• Continuous runtime monitoring at the workflow level (not the prompt level)
• Adversarial testing of agent behavior across the full tool/workflow/delegation/permission surface
• Compliance evidence generation aligned with emerging agentic AI security frameworks (CSA, OWASP, MITRE ATLAS)

This is the gap FailSafe ARC fills.

FailSafe ARC: Agentic Red Team & Compliance Platform

ARC is a continuous security platform for agentic AI deployments. It threat-models agent workflows before deployment, red-teams them continuously after deployment, and generates compliance evidence on demand.

The core principle: agents that red-team other agents. ARC deploys specialized adversarial agents against target workflows to discover vulnerabilities that static analysis and prompt-level inspection cannot find.

Three Modes, One Platform

Mode 1: Pre-Deployment Security Gate

Every agent workflow passes through ARC before reaching production.

Automated Workflow Threat Modeling

ARC ingests the workflow definition and automatically constructs a threat model:

• Maps every tool the agent can invoke, the permissions each tool requires, and the data each tool can access
• Identifies trust boundaries, where the workflow crosses from one trust domain to another, where it accepts external input, where it delegates to another agent
• Discovers workflow invariants, security properties that must hold for the workflow to be safe
• Identifies permission blast radius, the worst-case chain if the agent is manipulated

The threat model is not static. ARC uses an evolving model architecture, as the assessment discovers more about the workflow's behavior, new categories of investigation are dynamically generated. A workflow that handles financial data triggers economic state modeling. A workflow with multi-agent delegation triggers trust boundary analysis. The system discovers what questions to ask based on what the workflow actually does.

Adversarial Simulation

ARC spawns red-team agents that attempt to manipulate the workflow through every layer:

• Tool manipulation: Can the agent be driven to invoke tools in unintended sequences? Can tool parameters be influenced through crafted inputs? Can the agent be made to use a high-privilege tool when a low-privilege alternative exists?
• Workflow state attacks: Can workflow steps be skipped? Can the agent be driven into states the designer didn't anticipate? Can a valid-looking input cause the workflow to branch into an unintended path?
• Delegation poisoning: Can one agent's output be crafted to manipulate the downstream agent's behavior? Can adversarial context propagate through multi-agent handoffs?
• Permission escalation: Can tool permissions be chained to achieve an effect no single permission was meant to allow?

CI/CD Integration

ARC integrates into agent deployment pipelines as a security gate. Workflows that fail assessment don't deploy. Findings feed back to the developer with specific workflow-level fixes, not generic recommendations, but "this tool permission should be scoped to read-only" or "this workflow needs a human-approval checkpoint between triage and remediation."

Mode 2: Runtime Red Teaming

Once an agent is deployed, ARC monitors and tests continuously.

Behavioral Baseline

ARC observes the agent's normal execution patterns and builds a behavioral model:

• Which tools it calls, in what order, with what parameters, at what frequency
• Normal workflow state transitions and execution paths
• Expected data flow patterns, what data enters the workflow, where it goes, what exits
• Cross-agent interaction patterns in multi-agent deployments

Continuous Drift Detection

When agent behavior deviates from its baseline, ARC flags it:

• "This agent started calling the database directly instead of going through the API layer after Tuesday's model update"
• "Tool invocation frequency tripled, possible runaway loop or manipulation"
• "This agent's workflow is consistently skipping the approval step that was present in its original definition"
• "Cross-agent context passing changed shape, Agent B is receiving fields it didn't receive before"

This operates at the workflow level, complementing AI runtime security tools at the prompt level. A model update that doesn't trigger any prompt-level alerts may still cause behavioral drift in tool usage patterns or workflow state transitions.

Live Adversarial Testing

ARC periodically runs adversarial simulations against production workflows in sandboxed shadow mode. This catches:

• Regressions from model updates (the LLM handles a prompt differently after fine-tuning, causing a workflow branch to behave differently)
• Environmental drift (upstream API responses changed, causing the agent to select different tools)
• New attack vectors that emerge as the agent's data distribution evolves

Attack-Defense Feedback Loop

Vulnerabilities discovered by ARC's adversarial agents in pre-deployment or live testing automatically feed back into the runtime monitoring baseline. Every new attack pattern becomes a new detection signature. Every environmental change that exposes a new vulnerability is incorporated into the next round of adversarial testing. The system evolves its defenses as fast as the threat landscape shifts.

Cross-Layer Intelligence

ARC is designed to operate alongside AI runtime security tools, not replace them, and the two layers make each other smarter through bidirectional intelligence sharing.

When an LLM firewall detects a prompt injection attempt at the model boundary, that signal feeds into ARC. ARC uses it to trigger targeted adversarial testing of the specific workflow that was attacked: what would have happened if the injection succeeded? Which tools would the agent have invoked? What workflow state would it have entered? What downstream agents would have inherited the corrupted context? The prompt-level signal becomes a workflow-level investigation.

In the other direction, when ARC discovers a workflow vulnerability, a tool permission chain that enables escalation, or a delegation path that propagates corrupted context, it generates detection signatures that feed back to the LLM firewall. The firewall can now catch the specific input patterns that would trigger the workflow-level vulnerability, blocking the attack at the prompt boundary before it ever reaches the agent.

The firewall sees the prompt layer. ARC sees the workflow layer. Intelligence flows both ways, each layer's findings sharpen the other's detection.

Mode 3: Compliance and Governance

Framework Alignment

Regulatory bodies and standards organizations are publishing controls for securing agentic AI systems. ARC automatically maps every deployed workflow against these controls and generates compliance evidence:

• Agent identity management, tracks unique agent identities tied to supervising agents or users, maintaining full accountability chains
• Threat modeling requirement, automated workflow threat modeling directly satisfies requirements to map agentic workflows to identify where threat actors could exploit vulnerabilities
• Human accountability, maps human-in-the-loop checkpoints in each workflow and flags workflows where autonomous action occurs without adequate human oversight
• Tool access controls, verifies that agent tool permissions are minimally scoped and consistent with the workflow's intended function

ARC maps to CSA's Securing Agentic AI Framework, OWASP Top 10 for LLM Applications, MITRE ATLAS, and NIST AI RMF, generating evidence artifacts for each framework on demand.

Audit Trail

Complete workflow-level audit trail: which agent did what, when, using which tool, authorized by which workflow, triggered by which input. Not just LLM call logs, full action-level traceability.

Sovereign Execution Verification

For sovereign AI deployments, ARC traces every data flow through every agent action to verify that data sovereignty guarantees are maintained across all execution paths. Flags any workflow path that could route data outside approved boundaries, including indirect paths through tool calls, API integrations, or cross-agent delegation.

Architecture

The Threat Model Evolver

Traditional security tools, including AI-focused ones, decide what to look for before the assessment starts. The checklist, the rules, the attack patterns are predetermined. A STRIDE-based threat model applies the same six categories to every system. An OWASP scan checks the same top 10 against every application.

ARC's Threat Model Evolver (TME) is fundamentally different. It discovers what to look for based on what it observes. The categories of investigation emerge from the engagement itself.

The process:

1. Start with a minimal set of seed analysis prompts (trust boundaries, entry points, assets)
2. Run the prompts against the target workflow, produces an initial threat model
3. The threat model generates targeted hypotheses about potential vulnerabilities
4. Red-team agents execute the hypotheses against the workflow
5. The TME analyzes results and asks: "What structural aspects of this workflow do I still have no model for?"
6. New analysis prompts are dynamically generated for the newly discovered aspects
7. Loop, the model gets deeper and more specific to this particular workflow with each round
8. Exit when the TME identifies no remaining model gaps

ARC doesn't apply a generic checklist to every workflow. It builds a security model tailored to each specific workflow's architecture, tools, data flows, and trust boundaries, and the model evolves as it discovers more about the workflow. A workflow that handles financial data triggers economic state analysis. A workflow with multi-agent delegation triggers trust propagation analysis. A workflow with external API integrations triggers third-party trust analysis. None of these categories are predetermined, they emerge from observation.

Red-Team Agent Arsenal

ARC deploys specialized adversarial agents for each attack surface:

• Tool Manipulation Agents, craft inputs designed to influence tool selection and parameters
• State Machine Agents, systematically explore workflow state transitions, looking for unintended paths and skip conditions
• Delegation Poisoning Agents, generate adversarial context that propagates through multi-agent handoffs
• Permission Chain Agents, attempt to combine individually-safe tool permissions into escalation chains
• Behavioral Drift Agents, probe for changes in workflow behavior after environmental changes
• Chain-of-Thought Agents, inject forged reasoning artifacts to test whether the agent's decision logic can be biased through manipulated context

Each agent produces structured evidence, the specific input that triggered the unintended behavior, the execution path that was followed, and the impact assessment.

Integration

ARC is designed to integrate with any agentic AI orchestration platform with minimal friction. Four deployment modes are supported, choose the one that fits your infrastructure.

SDK Mode Lightweight library that instruments agent tool calls, workflow state transitions, and cross-agent delegations. Emits structured telemetry to ARC. Provides the deepest visibility with fine-grained developer control over what is instrumented. One-line initialization.

Container / Sidecar Mode Deploy ARC as a container alongside the agent runtime. Zero code changes required. Observes agent behavior via shared logging, network telemetry, or OpenTelemetry trace collection. Ideal for Kubernetes-based deployments and environments where modifying agent code is not feasible.

API Mode ARC exposes a REST API. The orchestration platform sends workflow definitions and execution telemetry; ARC returns threat models, findings, compliance status, and behavioral baselines. Platform-agnostic, works with any system that can make HTTP calls.

CI/CD Plugin Mode Pre-deployment security gate that runs automatically when workflow definitions are created or updated. Integrates with standard CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins). Blocks deployment of workflows that fail security assessment. Returns findings inline with the development workflow.

All four modes feed into the same ARC engine. Pre-deployment threat modeling, runtime monitoring, adversarial testing, and compliance reporting work identically regardless of integration mode. Organizations can start with one mode and expand as their deployment matures.

Why FailSafe

FailSafe has built and operated multi-agent security assessment systems on production engagements across Web3 and Web2 domains. The methodology underpinning ARC is not theoretical, it has been validated on real systems with real results.

Proven Multi-Agent Security Methodology

FailSafe's SWARM system uses multiple specialized AI agents working in a structured pipeline, comprehension, hypothesis generation, deduplication, validation, and deep-dive chain discovery, to find vulnerabilities in complex systems. Deployed on production engagements including:

• OpenEden (Financial Infrastructure): Agentic assessment of API security, trust boundaries, cross-service authentication, and economic state consistency across a multi-component financial system. The threat model evolved during the engagement, new analysis categories were dynamically generated when the system discovered architectural patterns that warranted them.

• Metacomp (Digital Asset Exchange): Multi-agent security assessment of trading infrastructure, order management, and custody systems. Threat modeling across trust boundaries between exchange, custody, and settlement components.

• Terminal 3 (Identity Infrastructure): Security assessment of decentralized identity and credential verification systems, trust boundary analysis across issuers, holders, and verifiers.

• MetaSig (Multi-Signature Infrastructure): Agentic analysis of multi-party signing workflows, authorization chains, and key management systems, security assessment of coordination logic between multiple signers.

• Nodo (Infrastructure Platform): Security assessment across multiple audit cycles, covering API security, access control, and cross-service trust boundaries.

The structured pipeline, build a threat model, generate hypotheses, deduplicate, validate with evidence, consistently finds business logic and chain vulnerabilities that automated scanners miss. Across 500+ engagements, the methodology has proven that the most critical findings come from understanding the system's architecture deeply enough to discover novel attack categories, not from applying predetermined checklists.

Evolving Threat Model Methodology

The Threat Model Evolver at ARC's core was developed through direct experience. Across multiple engagements, the most critical findings came not from applying a predetermined checklist but from discovering new categories of investigation as the assessment progressed. The TME automates this process, the system that generates the right questions is more valuable than any fixed set of answers.

Agentic Systems Building Agentic Systems

FailSafe builds and operates multi-agent AI systems daily. The team understands the trust boundaries, failure modes, and coordination challenges of agentic architectures from the builder's perspective, not just the assessor's perspective. This dual understanding, how agents are built and how they can be broken, is what makes ARC effective.

Summary

Organizations are deploying autonomous AI agents that act on critical infrastructure. The security challenge isn't just protecting the LLM, it's ensuring that the entire agent workflow, from tool permissions to multi-agent delegation to state machine logic, behaves as intended under adversarial conditions.

AI runtime security tools protect the model boundary. ARC protects everything beyond it: continuous, automated, workflow-level red teaming across the full agent attack surface. Every workflow is threat-modeled before deployment. Every deployed agent is continuously tested. Every compliance requirement is mapped and evidenced.

Agents that red-team other agents, built by a team that builds and breaks agentic systems for a living.