Executive Outcomes
Zero‑Trust
Strong isolation, least‑privilege tool use
Private‑by‑Design
Per‑tenant indices + encryption at rest/in use
Provable
Auditability, reproducibility, and evidence packs
Resilient
Federated learning + DP; safe rollbacks
Reality: Security posture is only as strong as data boundaries (snapshots, logs, configs) and tooling policies (intent verification, approval gates).
1) Deep Theory — Threat Model for LLMs in Network Assurance
Prompt InjectionData PoisoningModel InversionSupply‑ChainSide‑Channels
- Prompt Injection: Attacker crafts inputs to subvert tools. Mitigation: tool allowlist, schema‑validated outputs, instruction firewalls, content filters.
- Data Poisoning: Malicious configs/logs skew behavior. Mitigation: signed snapshot feeds, anomaly filters, canary evals.
- Model Theft/Inference: Extract training secrets. Mitigation: per‑tenant RAG, no cross‑tenant training unless privacy‑preserved.
- Policy Bypass: Skipping intent checks. Mitigation: mandatory verification gates + CAB approvals.
IP Fabric mapping: Snapshots are the “truth substrate”; sign them and version them. Intent Verification becomes the security oracle for every answer/action. fileciteturn4file2
2) Zero‑Trust Architecture — Policies over People
Security Policy (excerpt)
Principles: least‑privilege, deny‑by‑default, evidence‑first
Boundaries: per‑tenant indices (configs/intents/snapshots), network policies
Tooling: only read‑only tools in analysis mode; write actions require twin simulation + approval
Verification: all answers must include citations to intents/snapshots; reject otherwise
Rotation: keys/tokens rotated every 90d; model manifests signed and pinned
Pseudo‑code: Tool Allowlist & Verification Gate
function SECURE_ANSWER(query):
allowed = ["latest_snapshot","path_lookup","intent_results"]
plan = lrm.plan(query)
for step in plan.steps:
if step.tool not in allowed: deny("tool not allowed")
ctx = gather(plan, tools=allowed)
draft = llm.generate(query, ctx)
if not verify.intents(draft, ctx): return "REJECT: unverifiable"
return with_citations(draft, ctx)
3) Privacy by Design — Differential Privacy (DP) & Federated Learning (FL)
- DP: Bound leakage when sharing metrics or model updates (ε‑budget, composition).
- FL: Train across customers without centralising data; use secure aggregation + poisoning checks.
- When to use: Global anomaly signatures, generic command understanding — never push raw tenant data.
Contract: POST /fl/round
Body: { "model_id":"risk-v17","weights":"...","dp":{"epsilon":1.0},"evidence":["hash(snapshot)","eval:canary"] }
Returns: { "accepted":true,"aggregated_id":"risk-v18","notes":"poisoning checks passed" }
Spec: Privacy Budget Ledger
Ledger Row:
{ "tenant":"acme","operation":"share-metric","epsilon":0.2,"timestamp":"...","remaining_epsilon":1.6 }
4) Enterprise Scale — LLMOps as SRE
- SLOs: p95 latency, answer faithfulness ≥ target, change decision time.
- Observability: prompt traces, retrieval quality (Context Precision/Relevancy), per‑tenant dashboards.
- Controls: canary prompts/models, kill‑switch, rollout rings, cost quotas.
Runbook: Deploy
1) Shadow‑mode → 2) Human‑approved → 3) Low‑risk automation → 4) Broader scopes with SLO gates
Evidence: snapshot diffs + intent results + blast radius score
5) Compliance & Audit — Evidence or it didn’t happen
Audit Record (schema)
{ "trace_id":"...","actor":"ai|human","tenant":"...","inputs":{q,ctx_ids},"decision":"APPROVE|REJECT","evidence":["intent:segmentation:pass","snapshot:SNAP_..."],"rollback_plan":"url","timestamp":"..." }
Policy‑to‑Control Mapping
- Data residency → per‑tenant storage, regional pinning
- Least privilege → tool allowlists, RBAC
- Integrity → signed snapshots/manifests
- Accountability → immutable audit store + review SLA
Week 5 Deliverables
- Zero‑Trust Security Policy + Verification Gate pseudo‑code
- DP/FL contracts incl. privacy budget ledger
- LLMOps SRE pack: SLOs, canaries, observability dashboards
- Compliance schemas + policy‑control mapping