Architecture brief
Threat Model · Governed Intelligence Architecture
AI systems do not fail only through inaccurate answers. They fail when identity is unclear, boundaries collapse, memory is poisoned, tools are misused, data escapes, providers become unchecked authorities, or actions occur without reconstructable evidence.
Threat thesis
The central security problem in AI is not simply malicious output. It is uncontrolled authority. An AI threat model must account for every point where a system can receive instructions, retrieve context, generate influence, call tools, persist memory, or alter operational state.
Why the threat model matters
Instruction Risk
Prompts can carry hidden instructions, social engineering, policy bypass attempts, or malicious context.
Memory Risk
Persistent systems can be manipulated if poisoned context is stored, retrieved, and trusted later.
Tool Risk
Tool access turns language into operational action and requires strict authorization boundaries.
Provider Risk
Model vendors supply inference. Governance, identity, memory, and authority must remain above provider control.
The architecture
Threat 01
A prompt attempts to replace the system’s governing instructions, manipulate context, bypass policy, or cause the AI to reveal, ignore, or corrupt protected behavior.
Threat 02
A model or agent attempts to invoke tools outside the appropriate scope, act without approval, access unauthorized systems, or perform an irreversible operation without review.
Threat 03
Malicious or inaccurate context is persisted, retrieved later, and treated as trusted memory.
Threat 04
Private or institutional data is exposed through prompts, model responses, retrieval, logs, tool calls, or provider routing.
Threat 05
The system confuses suggestion, permission, delegation, approval, and execution authority.
Threat 06
Identity, memory, and governance become coupled to a provider whose incentives, controls, or availability may change.
Threat 07
A consequential result cannot be explained because the system failed to preserve inputs, context, policy outcomes, model route, or execution evidence.
Threat 08
Agentic systems continue operating beyond intent, scope, budget, safety limits, or authorized objective.
Defense model
A model-level safety setting is not an enterprise threat model. Governed AI requires repeated evaluation across identity, context, policy, routing, execution, persistence, and audit.
1. Authenticate
Verify the actor, role, institution, and delegated authority.
2. Classify
Determine whether the request is conversational, informational, operational, risky, or adversarial.
3. Govern
Apply policies, permissions, risk scores, and human review requirements.
4. Route
Select approved model, provider, memory store, tool, or execution path.
5. Contain
Limit tool scope, context access, memory writes, execution privileges, and blast radius.
6. Preserve
Record enough evidence to reconstruct consequential decisions later.
Threat map
Prompt Injection → Input Control
Instruction hierarchy, prompt scanning, policy separation, and contextual validation.
Memory Poisoning → Persistence Control
Memory write approval, source attribution, confidence scoring, review, and deletion rights.
Data Exfiltration → Data Boundary
Data classification, least-privilege retrieval, redaction, provider routing policy, and output review.
Tool Misuse → Execution Gate
Tool registry, scoped permissions, human approval, rate limits, and execution logs.
Autonomous Drift → Runtime Containment
Budget limits, step limits, objective checks, approval checkpoints, and kill switches.
Untraceable Decisions → Forensic Replay
Capture identity, context, policy, route, model, tool, execution, output, and persistence events.
Rita + Palladium + Origin
Rita defines the threat-aware governance logic: boundaries, policy hierarchy, authority constraints, risk classification, and escalation rules.
Understand Rita
Palladium operationalizes defense for organizations: runtime enforcement, model routing, tool gating, memory controls, telemetry, containment, incident review, and forensic replay.
Explore Palladium
Origin reduces personal AI risk by protecting identity, memory, consent, context, and continuity above raw model inference and outside provider lock-in.
Explore OriginArchitecture pages
Next: Forensic Replay
Threat modeling identifies what can go wrong. Forensic replay proves what actually happened — preserving identity, context, policy outcomes, model routes, tool calls, outputs, and persistence events so consequential AI decisions can be reconstructed later.
