AETHERWARD

Service / 02

AI Threat Modeling, Detection & Runtime Security

Connected threat models, behavioral detections, and runtime controls for AI systems whose actions cannot be judged one event at a time.

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The objective

See the harmful sequence forming across model, identity, memory, and tools—and retain the authority to stop it.

We decompose the system into identities, trust zones, data flows, tool calls, model decisions, memory changes, and human approvals. That creates a threat model teams can use for engineering, detection, governance, and incident response instead of a static compliance diagram.

Detection design focuses on behavior over time: legitimate actions that become dangerous through sequence, timing, permission composition, cross-tool escalation, or context manipulation. Recommendations connect telemetry to containment, investigation, and recovery decisions.

Often requested as

Agentic AI threat modelingAI detection engineeringRuntime protectionAI firewall reviewAI SOC / observability design
Assessment surface

What we cover

  1. 01Model, agent, orchestrator, MCP, and A2A trust boundaries
  2. 02Agent identity and non-human identity authorization
  3. 03Behavioral attack-chain and sequence analysis
  4. 04Tool misuse, cross-agent escalation, and confused-deputy paths
  5. 05Telemetry, reasoning trace, audit, and evidence requirements
  6. 06Anomaly, drift, misuse, and policy-violation detection
  7. 07Runtime policy enforcement and high-risk action approval
  8. 08Containment, rollback, and response authority design
The handoff

What you receive

  1. 01Living system threat model
  2. 02Prioritized attack-path library
  3. 03Telemetry and observability specification
  4. 04Detection logic and response playbooks
  5. 05Runtime control and containment roadmap

Designed outcomes

Connected attack pathsDetection-ready telemetryBounded runtime authorityFaster investigation
How it works
01

Decompose

Document system roles, identities, data flows, protocols, trust zones, expected behavior, and failure authority.

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Connect paths

Trace credible attacks across components, including sequences where each isolated action appears legitimate.

03

Design signals

Specify the logs, traces, provenance, state, and correlation required to detect those paths.

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Operationalize

Connect detections to human review, automated containment, evidence preservation, and recovery.

Operating boundary: All work is performed within explicitly authorized scope. High-risk actions remain human-approved, and findings are communicated with evidence, uncertainty, and practical remediation context.

Research-driven security

Built for attack surfaces traditional security models were not designed to see.

Aetherward’s assessment methods are informed by continuous internal research into behavioral attack chains, legitimate-tool abuse, permission composition, cross-tool escalation, context manipulation, model-to-tool boundary failures, poisoning, and abnormal agent behavior.

Explore Aetherward research ↗
Additional capabilities

Fixed-scope or project-based

Security Automation Engineering

Custom security tooling for teams that need specialized automation without building a full internal platform.

Triage automationDetection toolingScope-aware scannersEvidence collectionThreat-intelligence workflowsAnalyst tooling

Recurring engagement

Ongoing AI Security & Architecture Advisory

Independent review as models, data, integrations, vendors, threats, and business requirements change.

Architecture reviewIntegration reviewThreat updatesRelease gatesSecurity driftDecision support

Building or deploying an AI system?

Assess it. Secure it. Build it. Repair it. ↗