FawzoozGive each AI task only the minimum context it requires.
What the agent must do, which model it goes to, and whether a human verifies the result.
For each item: how sensitive it is, whether the task truly requires it, and whether its source can be trusted.
Tick the Cognitive Security Stack controls in place, then copy the scoped context manifest.
The scoper
Anything not required is left out, secrets are never passed, and sensitive items travel only as scoped, ephemeral tokens. The result updates as you go.
Why Zero Trust is not enough
In vibe orchestration, AI is persistent: it makes decisions, executes logic and manages environments. This exposes a new threat surface — the cognitive layer. Cognitive security is the discipline of protecting that layer, and the human operating system around it, from manipulation, deception and systemic disruption.
Core concept
Zero Trust verifies every request and restricts privilege by identity — insufficient for AI that reasons, reflects and writes its own logic. LCAC extends Zero Trust into the reasoning layer by restricting the data supplied to a model’s context to the absolute minimum a task requires, preventing context leakage and manipulation.
excluded. LCAC admits the absolute minimum a task requires.
always excluded; a tool that needs one gets a scoped, ephemeral token at execution.
included only as scoped, ephemeral tokens.
regulated data bound for an external model, a Critical-tier task with missing controls, or nothing left to run on.
any open warning (external model, untrusted source, subagent hand-over) or any missing stack control.
The paper states the LCAC principle and the stack; it sets no numeric thresholds. These rules are this tool’s operational reading of it.
Figure 11
Built on Least-Context Access Control and extending Zero Trust into the reasoning layer. Each layer is one of the controls in step three.
Logs the model’s reasoning states and decision paths without storing sensitive prompt content, giving an unalterable, auditable record of the AI’s “thought process”.
Prevents the ungoverned transfer of knowledge, intent or system variables between subagents, mitigating data drift and cross-context bias.
Prompts, system files and retrieval vectors are broken into scoped, ephemeral tokens, so data stays isolated and is destroyed after execution.
LCAC extends Zero Trust into the reasoning layer by restricting the data supplied to a model’s context to the absolute minimum a task requires, preventing context leakage and manipulation.
Zero Trust verifies every request and restricts privilege by identity — insufficient for AI that reasons, reflects and writes its own logic.
Figure 8
Every declared instance is scored on a five-dimensional taxonomy — data sensitivity, decision impact, processing transparency, organizational function and third-party supply-chain reliance — and placed in one of four risk tiers. The scoper reads the first two dimensions from what is left in the context.
Network restriction and incident investigation
Criteria: Regulated or confidential data, autonomous decisions, high-stakes settings
Substitute an approved enterprise equivalent
Criteria: Confidential data or determinative business decisions
Onboard with vendor due diligence and DPAs
Criteria: Internal data, advisory output with human verification
Approved catalog plus targeted training
Criteria: Public, non-sensitive data; no core business logic
OWASP Top 10 for LLM Applications (2025)
Each category corresponds to an exposure pathway that shadow tools open by default. These are the ones that grow with every unnecessary item in a model’s context.
Adversarial inputs (direct or indirect) that overwrite system prompts
Impact: Safety bypasses, unauthorized commands, malicious code in the developer environment
Leakage of proprietary algorithms, PII or architecture via output
Impact: Exposes corporate secrets, voids IP and triggers regulatory penalties
Broad permissions to call APIs or databases with no human in the loop
Impact: Rogue agents delete production data or alter access control
Disclosure of the instructions that bound the model’s behaviour
Impact: Adversaries reverse-engineer defences, steal prompt IP, craft bypasses
Flaws in generation, storage or retrieval within RAG
Impact: Poisoned vector stores surface malicious context and instructions
Overloading costly inference to overwhelm services (Denial of Wallet)
Impact: Billing spikes, exhausted cloud capacity, severe latency
Executive recommendation 05
Adopt Least-Context Access Control, cognitive sandboxing and a context registry for every agent.
About the checklist
From my white paper Vibe Orchestration (2026), Section 7, “Vibe Orchestration and the Cognitive Security Stack” (Figure 11), with the risk tiers of Section 5 (Figure 8), the AI usage policies of Section 6, and OWASP Table 3.
Elgendi, M. F. (2026). Vibe Orchestration. A white paper. www.fawzooz.ai
