AI, LLM and real-time · AWS · Databricks · IoT · MAESTRO
A reference architecture for council, utility, building and industrial assets whose sensors stream telemetry through AWS IoT into a Databricks lakehouse, where foundation models explain anomalies, draft maintenance work orders and answer questions in plain language. Any command that would change a device waits for a named person. The same page maps the design to the seven CSA MAESTRO layers and to the OWASP Top 10 for LLM and agentic applications.
A reference architecture from EITSAA, not a named client system. Service limits, prices and product features change, so confirm current details before you commit to a design.
Streaming telemetry into a lakehouse is a well understood data problem. Adding a large language model that can explain an anomaly, draft a work order or propose a device command changes the threat model. The model reads untrusted sensor text and maintenance notes, it chooses tools, and a fluent explanation can talk a busy operator into approving something they should not. STRIDE still applies to the APIs, certificates and networks. CSA MAESTRO adds a layer-by-layer view of the agent itself.
MAESTRO (Multi-Agent Environment, Security, Threat, Risk and Outcome) is the Cloud Security Alliance's agentic AI threat modelling framework, published in February 2025. It defines seven architectural layers, with security and compliance as a vertical layer that spans the others, plus a cross-layer pass for chains that single-layer analysis misses. The walk-through below applies those layers to an AWS and Databricks design that many councils, utilities and industrial operators are now considering: IoT devices, a governed lakehouse, and an LLM operations assistant that must never actuate plant on its own.
A seven-layer threat modelling method for agentic AI: foundation models, data operations, agent frameworks, deployment infrastructure, evaluation and observability, security and compliance (vertical), and the agent ecosystem, plus cross-layer analysis of cascading threats.
Cloud Security Alliance, Agentic AI Threat Modeling Framework MAESTRO (Ken Huang, February 2025), and the MAESTRO playbook layer descriptions.
IoT here means sensors, meters, cameras and gateways that report the physical world. OT is the subset that can also change that world: pumps, valves, switchgear, building management and industrial control. ASD describes OT as systems that detect or cause a direct change to the physical environment through monitoring or control.
ASD Essentials for OT (published 15 October 2026).
Large pre-trained models, usually transformers, adapted to many tasks by prompting, retrieval or fine-tuning. In this design they explain telemetry, retrieve manuals and draft text. They are not a substitute for a purpose-built anomaly detector, and they are not a controller.
Consistent with NIST AI 600-1 and with Databricks Foundation Model APIs and Amazon Bedrock model serving.
Any write that would change a device, setpoint, schedule or safety-related process pauses until a named person sees the exact command and approves it. Preferred practice for OT is to raise a work order for field staff rather than send a remote command at all.
Aligned with ASD Essentials for OT (controlled remote access, treat OT devices as privileged), IEC 62443 zoning, and OWASP ASI02 tool misuse and ASI09 human-agent trust exploitation.
Telemetry flows from field devices through AWS IoT into Databricks, where governed tables and search feed an operations assistant. Foundation models produce explanations and drafts. Work orders and device commands leave the assistant only after a person approves them.
Sensors, meters, cameras and plant report through a gateway or directly. Where you use AWS IoT Greengrass, the core can filter, batch and run optional local models so raw noisy readings need not all leave the site. Consumer and industrial devices should have unique identities; they must not share a default password.
Greengrass, or the device itself, publishes MQTT to AWS IoT Core over mutual TLS with an X.509 certificate. An IoT policy scopes each principal to the topics of that asset. AWS IoT Device Defender watches for unusual connect and publish patterns.
An AWS IoT rule forwards payloads to Amazon Kinesis Data Streams or to Amazon MSK. Databricks documents both AWS IoT Core and Amazon Kinesis as streaming ingest paths on its AWS reference architecture, and it can read Kinesis or Kafka with Structured Streaming.
Lakeflow pipelines (the Databricks product formerly called Delta Live Tables) ingest the stream into bronze tables, apply quality expectations, and build silver and gold tables: clean telemetry, asset context and features such as rolling statistics used by anomaly detectors.
Unity Catalog governs the Delta tables, feature tables, volumes, models and lineage. Access policies and classification tags travel with the data rather than living only in a notebook.
A Databricks AI Search index (formerly Vector Search) stays in sync with a governed table of manuals, procedures, past tickets and summarised events, so retrieval has the same lineage and deletions as the source table.
An operator signs in with SSO and MFA, or an anomaly alert opens the assistant with the asset already in context. The assistant never receives a broader identity than the person in front of it.
For a question or an alert the assistant retrieves from AI Search with filters built from the user's entitlements and the asset they are allowed to see. Retrieved text is wrapped as untrusted reference material, not as instructions.
The assistant calls a foundation model through Databricks Model Serving and Foundation Model APIs, or through Amazon Bedrock with Guardrails, to explain the detector's score, compare the event with similar history, or answer a natural-language query with citations.
If maintenance is needed, the assistant drafts a work order in the CMMS or asset system. Creating or assigning that ticket is a write: the graph interrupts, shows the fields, and continues only after approval unless the organisation has explicitly allowed a low-risk draft-only API.
If someone asks the assistant to change a setpoint, reboot a gateway or run any other device command, the assistant proposes an AWS IoT Job or device shadow update and stops. It cannot send the command itself.
A named approver sees the exact topic, payload and target devices, with the evidence the assistant used, and only their approval publishes the job back through IoT Core. Safety-related or high-impact OT changes should normally become a work order for people on site instead of a remote command.
| What it covers in this design | Main threats | Controls and mappings | |
|---|---|---|---|
| 1. Foundation models | Databricks Model Serving and Foundation Model APIs (Mosaic AI), and/or Amazon Bedrock models with Guardrails. Optional fine-tuning or custom weights registered in Unity Catalog. | Jailbreaks, poisoned fine-tuning data, a vendor model change that shifts behaviour, and leakage of prompts or telemetry into a model that is not approved for that data. | Pin model IDs and versions, evaluate before promotion, keep customer content out of provider training where the contract allows it, and confirm residency (Databricks Foundation Model APIs are a Designated Service that uses Databricks Geos; Bedrock availability and inference profiles vary by Region). Maps to OWASP LLM03 supply chain, LLM04 data and model poisoning, and ASI04 agentic supply chain. |
| 2. Data operations | AWS IoT rules into Amazon Kinesis Data Streams or Amazon MSK, Lakeflow pipelines (formerly Delta Live Tables) into Delta tables, Unity Catalog, feature tables, and Databricks AI Search (formerly Vector Search) over manuals, tickets and event summaries. | Poisoned manuals or operator notes, spoofed or replayed telemetry, prompt injection hidden in retrieved text, and an index that returns another tenant's or another crew's assets. | Device identity before ingest, pipeline quality expectations, classify and minimise personal information, carry asset and access tags into the index, filter every query by the user's entitlements, and treat retrieved text as untrusted data. Maps to OWASP LLM01 prompt injection, LLM08 vector and embedding weaknesses, and ASI06 memory and context poisoning. |
| 3. Agent frameworks | The operations assistant: planning, tool choice and prompts. Tools include retrieval, drafting a CMMS work order, and proposing an AWS IoT Job or device shadow update. | Goal hijack through a malicious note or alert, tool misuse (bulk commands, the wrong asset), and excessive agency if the model can call actuation directly. | A fixed graph with a step budget, typed tools, no credentials in the prompt, deny-by-default tool policy, and a hard interrupt before any write. Maps to OWASP LLM06 excessive agency, ASI01 agent goal hijack and ASI02 tool misuse. |
| 4. Deployment and infrastructure | Field networks and AWS IoT Greengrass at the edge, AWS IoT Core, streaming, Databricks on AWS reached over PrivateLink, and the assistant runtime. | Shared or default device passwords, over-privileged IoT policies or IAM roles, a path from the internet onto the OT network, and unexpected code execution from generated commands or notebooks. | Unique X.509 device identities, least-privilege IoT policies, outbound-only OT egress, network zones and conduits, no standing admin from the assistant, and no shell tool on production devices. Maps to ASI03 identity and privilege abuse, ASI05 unexpected code execution, ASD Essentials for OT and IEC 62443. |
| 5. Evaluation and observability | Lakehouse pipeline event logs, Databricks traces, AWS IoT and CloudWatch metrics, CloudTrail, and a review UI that shows what the assistant saw, retrieved and proposed. | Missing traces so an action cannot be reconstructed, flooded review queues, and metrics that look healthy while answers silently drift. | End-to-end traces across ingest, retrieval, model and tools, an immutable audit archive, evaluation gates before release, and staffing for the approval queue. Maps to ASI08 cascading failures and the MAESTRO questions on reconstructability and HITL overload. |
| 6. Security and compliance (vertical) | Identity, policy, privacy, records and sector rules that cut across every other layer: staff SSO, device identity, encryption, residency and OT safety obligations. | The assistant inheriting a wider role than the signed-in person, unclear ownership of AI decisions, and personal or location data used without an assessment. | Separate identities for people, devices and the assistant; MFA for operators and approvers; customer-managed keys; a privacy assessment before occupancy or identifiable telemetry is used; and an AI inventory entry. Maps to ASI03, the Australian Privacy Principles, and ASD Essentials (OT and Essential Eight). |
| 7. Agent ecosystem | Devices and gateways, operators, approvers, the CMMS or asset system, manuals from vendors, and any other agent or MCP tool later connected. | A compromised partner feed or MCP server, operators approving a fluent but wrong command (trust exploitation), and a runaway or shadow agent that issues jobs. | Reviewed, versioned tool definitions, authenticated device and user channels, show the raw command not only the explanation, kill switches, and no unsanctioned agents on the OT path. Maps to ASI07 insecure inter-agent communication, ASI09 human-agent trust exploitation and ASI10 rogue agents. |
| Cross-layer | Chains that start in one layer and finish in another, which is where MAESTRO says the highest-value findings usually appear. | Spoofed telemetry or a poisoned manual (layer 2) steers the model (layer 1), the assistant drafts a harmful job (layer 3), and a tired approver signs it (layer 7), which then reaches plant (layer 4). | Break the chain at several points: authenticated ingest, untrusted retrieval, no direct actuation, an approval screen that shows arguments and source excerpts, and a separate detector that does not rely on the LLM. Maps to MAESTRO cross-layer analysis and ASI08 cascading failures. |
MAESTRO mapping
The same components, grouped by MAESTRO layer rather than by request flow. Security and compliance (layer 6) is vertical: it applies to device identity, staff SSO, encryption, privacy and OT rules on every other layer. After you walk each box, look for cross-layer chains, such as poisoned retrieval that becomes a fluent request to actuate plant.
Layer 7 starts the story: physical devices, the people who trust the assistant, and outside systems such as a CMMS or a future MCP tool. Compromise or confusion here is how harm leaves the screen.
Layer 4 is the path those devices take: Greengrass, IoT Core, streams and the assistant host. Mis-scoped certificates, open OT paths and over-privileged runtimes belong in this box.
Telemetry lands in layer 2 as Lakeflow pipelines, Delta tables, features and Unity Catalog governance. This is also where poisoned documents and spoofed points first become 'knowledge'.
Indexes and prompt stores stay in layer 2. A sync from a governed table keeps deletions and access tags aligned; the application still filters every query.
A person in the ecosystem (layer 7) asks a question or opens an alert. Their identity, not the model's, must decide what they are allowed to see and approve.
Layer 3 retrieves from layer 2. Indirect prompt injection is a layer 2 issue that becomes a layer 3 goal-hijack issue if the graph treats retrieved text as instructions.
Layer 1 generates the explanation or draft. Pin the model, evaluate it, and apply Guardrails or an equivalent screen so layer 1 cannot silently change behaviour or exfiltrate telemetry.
Drafts return to the ecosystem as work orders. Commands must not take this arrow to devices; they wait on an approver, which is the main control for ASI02 and ASI09.
Layer 5 records what every other layer did. If you cannot reconstruct a conversation and an approval, repudiation and silent failure hide in the gaps.
| Component | What it does | Why this choice |
|---|---|---|
| IoT and OT devices | Measure or, in OT, control the physical environment: water meters, road and building sensors, plant instrumentation and cameras. | Chosen by the asset owner. Specify unique device identity, updatable software and no universal default passwords (ETSI EN 303 645 / AS ETSI EN 303 645 and NIST IR 8259A). |
| AWS IoT Greengrass | Optional edge runtime: local processing, offline buffering and Stream Manager exports to the cloud. | Keeps high-frequency or site-local data on site and reduces what you stream. AWS documents Stream Manager exports to Kinesis Data Streams. |
| AWS IoT Core | Managed MQTT broker, device registry, X.509 authentication, message rules, Device Shadow and IoT Jobs. | Standard AWS device front door, with per-device certificates and policies rather than a shared key. |
| AWS IoT rules to Amazon Kinesis or Amazon MSK | Durable stream between IoT Core and the lakehouse. | Databricks lists AWS IoT Core, Amazon Kinesis and related services on its AWS reference architecture for streaming ingest. MSK suits teams that already standardise on Kafka. |
| Lakeflow pipelines | Declarative streaming and batch pipelines into Delta tables, with expectations and an event log. This is the current name of the product formerly called Delta Live Tables; existing DLT pipelines keep running. | Databricks' documented path for IoT and time-series lakehouses, including quality checks and exactly-once streaming processing. |
| Delta Lake and Unity Catalog | Governed storage for telemetry, features, manuals and models, with lineage and access control. | One catalogue for tables, volumes, features and registered models, which is what later retrieval and serving should read from. |
| Databricks AI Search | Managed hybrid vector and keyword index, typically kept in sync with a Delta table. | Avoids a second unsanctioned document store. Index permissions are not a substitute for a query filter from the user's groups. |
| Databricks Model Serving and Foundation Model APIs | Hosted foundation models on a serving endpoint, including pay-per-token and provisioned throughput modes, plus serving of registered custom or fine-tuned models. | Lets the assistant call an LLM next to the lakehouse data. Foundation Model APIs are a Databricks Designated Service that uses Databricks Geos for residency, so check the Geo for your workspace. |
| Amazon Bedrock and Bedrock Guardrails | Alternative or complementary model API, with content filters for denied topics, sensitive information and prompt attacks. | Useful when you already standardise on Bedrock, or want Guardrails in front of generation. Australian cross-Region inference profiles can keep supported model traffic within Australian Regions; confirm each model. |
| Operations assistant | A bounded agent that classifies the request, retrieves, explains and may draft a ticket or propose a command. | Keep planning in an explicit graph (for example LangGraph) so interrupts, step limits and tool policy are testable. The runtime can be your application tier, Databricks serving, or another controlled host. |
| CMMS or asset system | System of record for maintenance work orders. | Preferred sink for anything that should become human work on site, instead of a remote actuation. |
| AWS IoT Jobs and Device Shadow | The only cloud path that should change device desired state, and only after approval. | Keeps actuation in the IoT control plane, outside the model's credentials. |
| AWS IoT Device Defender, CloudWatch, CloudTrail and pipeline logs | Device behaviour anomalies, stream metrics, API audit and Lakeflow event logs. | You should be able to reconstruct what was sensed, retrieved, generated, approved and sent. |
Where telemetry is refined
Why. IoT streams need schema evolution, quality expectations and lineage. Databricks documents this pattern for IoT time-series lakehouses.
Trade-off. You operate a Databricks workspace as well as AWS IoT. Teams without Databricks should use AWS native analytics instead of adding a platform for its own sake.
How anomalies are detected versus explained
Why. LLMs are not reliable time-series detectors, and an explanation that invents a cause is safer when it cannot itself trip a control action.
Trade-off. You maintain a detector and an assistant. That is more work than prompting an LLM with raw traces, and it is the more defensible design.
Where generation runs
Why. Both are managed model APIs. Databricks keeps serving next to the lakehouse; Bedrock offers Guardrails and a range of foundation models under AWS IAM.
Trade-off. Feature and model availability differ by Region and, for Databricks Designated Services, by Geo. Confirm both before go-live.
How the assistant is allowed to act
Why. OWASP LLM06 and ASI02 describe the harm of excessive agency. ASD Essentials for OT treats OT devices as privileged and calls for controlled remote access.
Trade-off. Approval adds waiting time. For many assets that delay is the point; automate only after you have evidence the drafts are safe.
How OT and IT are separated
Why. IEC 62443 zones and conduits, and ASD Essentials for OT priority on the network boundary, exist specifically to stop an IT compromise becoming a physical one.
Trade-off. Some convenience features (interactive remote desktop onto controllers) are out of scope and should stay out.
Unique device identities, least-privilege IoT policies and IAM roles, OT/IT zoning, retrieval filters, untrusted-content handling, Guardrails or equivalent on generation, and human approval for every actuation.
Edge buffering on Greengrass, durable streams, Lakeflow retries and expectations, an assistant that still answers from last-known gold data if a model endpoint is down, and idempotent work-order drafts.
Filter and aggregate at the edge, incremental pipelines, a small retrieved top-k with hybrid search, and streaming answers. Keep raw high-frequency samples out of the prompt.
Cost follows message volume, pipeline compute, index size and model tokens. Edge filtering, gold summaries instead of raw traces in prompts, and smaller models for routine explanations keep it in check.
Versioned pipelines, indexes, prompts, tools and approval policy, released through CI with evaluation gates and a kill switch for the actuation tool.
Do not stream or embed unchanged data repeatedly, prefer summaries in context windows, and avoid calling a large model to detect conditions a rule or small model already handles.
Field and OT networks allow outbound MQTT (and any required Greengrass control-plane traffic) to AWS IoT Core through a controlled path such as a private link, VPN or tightly scoped NAT. There is no inbound path from the public internet or from the lakehouse onto controllers. Databricks is reached from AWS over PrivateLink in the same Region. The assistant runtime sits in private subnets and calls Model Serving or Bedrock through VPC endpoints where offered. Tool egress to the CMMS uses an allow-listed API.
Each device has its own certificate and an IoT policy limited to its topics, in line with NIST IR 8259A unique identity and ETSI EN 303 645. Operators and approvers sign in with the organisation's identity provider and MFA. The assistant has a workload identity that can read approved indexes and draft tickets, and it has no permission to call IoT Jobs. The approver's identity is what authorises a command, and that event is logged.
Classify telemetry and manuals at ingest. Occupancy, video and any personally identifiable readings need a privacy assessment before they enter training, prompts or long-term gold tables. Encrypt with customer-managed KMS keys on AWS and in Databricks where enabled. Set retention for raw bronze, gold features, traces and the audit archive, and make sure deletions in source manuals flow through to the search index.
| Control | How this design addresses it |
|---|---|
| CSA MAESTRO | The comparison table and the mapping diagram walk each of the seven layers plus cross-layer chains for this design. |
| OWASP Top 10 for LLM Applications (2025) | Indirect prompt injection (LLM01), supply chain (LLM03), data and model poisoning (LLM04), excessive agency (LLM06), vector and embedding weaknesses (LLM08) and misinformation (LLM09) are addressed by untrusted retrieval, pinned models, query filters, no direct actuation and cited explanations. |
| OWASP Top 10 for Agentic Applications (2026) | ASI01 to ASI10 are checked explicitly: goal hijack, tool misuse, privilege abuse, supply chain, unexpected code execution, memory poisoning, insecure inter-agent communication, cascading failures, human-agent trust exploitation and rogue agents. |
| ASD Essentials for OT | OT is isolated from general IT, remote access is controlled, OT devices are treated as privileged, and implementation starts with the network boundary and critical processes, with governance and visibility running throughout. |
| ASD Essential Eight | MFA for operators and approvers, restricted administrative privileges (no standing OT admin in the assistant), application control on engineering workstations, and logged administrative and approval actions. |
| IEC 62443 | Zones and conduits between field devices, edge, cloud ingest and the assistant; least privilege on each conduit; no uncontrolled path from the LLM runtime into a safety or control zone. |
| ETSI EN 303 645 / AS ETSI EN 303 645 | Baseline for consumer IoT: no universal default passwords, a means to update software, and publicly documented vulnerability handling when you specify or buy sensors. |
| NIST IR 8259 and 8259A | Manufacturer activities and the device cybersecurity capability core baseline, including unique identity, how the device is configured, data protection and event logging, used as a buying and onboarding checklist. |
| Australian Privacy Principles | Privacy impact assessment before identifiable or occupancy data is streamed, embedded or prompted; data minimisation; and documented cross-border processing if a Designated Service or model Geo sits outside Australia. |
Mappings show how the design supports each control. They are not certifications, and EITSAA does not claim IRAP assessment or compliance for any reference architecture.
| Failure mode | Mitigation |
|---|---|
| A device is cloned or its certificate is stolen | Per-device certificates and policies, Device Defender alerts on unusual connections, and the ability to revoke a single thing without rotating a shared secret. |
| Telemetry is spoofed or replayed to create a false incident | Authenticated ingest, timestamps and sequence checks in silver tables, and detectors that use more than a single raw reading. |
| A maintenance manual or ticket contains hidden instructions | Retrieved text is treated as data, generation is screened, tool policy ignores instructions found in documents, and actuation still needs a person. |
| The model invents a cause or a spare part | Ground answers in retrieved chunks with citations, keep the detector's score visible next to the explanation, and never let generated text become a command payload without review. |
| An operator approves a fluent but harmful command | The approval screen shows the raw job document, target assets and source excerpts, not only the narrative; high-impact OT changes default to a work order instead of remote actuation. |
| A pipeline or index falls behind during a flood of events | Edge buffering, stream autoscaling within quotas, quality expectations that quarantine bad data, and a visible freshness indicator on the assistant. |
| A model or prompt change silently lowers quality | An evaluation set of real alerts and questions gates release, with red-team cases for injection and attempted actuation. |
Cost grows with device message volume, how much raw data you keep and process, pipeline compute, the AI Search index, and tokens used by the assistant (long traces in the prompt multiply tokens quickly).
MAESTRO is the Cloud Security Alliance's seven-layer threat modelling framework for agentic AI, published in February 2025. An IoT assistant reads untrusted telemetry and documents and may propose actions that reach the physical world, so you need to look at the model, the data pipelines, the agent tools, the cloud and edge infrastructure, the logs, the compliance layer and the wider ecosystem of devices and people, then at chains across those layers.
No. In this architecture the model explains, answers and drafts. A named person approves any work order write or device command, and safety-related OT changes should normally become work for people on site rather than a remote job. The assistant's identity cannot call AWS IoT Jobs.
AWS IoT Core is the device front door. Databricks, when you already use it for data engineering, is a governed lakehouse for the streams that follow: Lakeflow pipelines, Delta tables, Unity Catalog, feature tables and AI Search. Databricks' own AWS reference architecture shows AWS IoT Core and Amazon Kinesis as ingest paths. If you have no Databricks estate, an AWS-native analytics path is usually simpler.
Databricks renamed Delta Live Tables to Lakeflow pipelines (via intermediate 'Lakeflow Declarative Pipelines' names). Existing DLT pipelines do not need a migration. Databricks Vector Search is now documented as Databricks AI Search; some API paths still say vector-search. This page uses the current product names and mentions the former ones so older material still makes sense.
You can place the AWS account, IoT resources and a Databricks workspace in Asia Pacific (Sydney). Foundation Model APIs are a Databricks Designated Service that uses Databricks Geos to manage data residency, and Bedrock model processing depends on the model and inference profile. Check the current regional and Geo pages for each model and feature before you promise residency.
They cover different parts of the same estate. IEC 62443 is the industrial automation and control-system series, centred on zones, conduits and capability levels. ASD Essentials for OT is Australian implementation guidance that sequences work, starting with the boundary and critical processes, and treats OT devices as privileged. ETSI EN 303 645, adopted in Australia as AS ETSI EN 303 645, is a baseline for consumer IoT devices (passwords, updates, vulnerability disclosure). NIST IR 8259 and 8259A add manufacturer and device-capability expectations you can write into a purchase.
Use MAESTRO to walk the design layer by layer, then check the findings against the OWASP Top 10 for LLM Applications (2025) and the OWASP Top 10 for Agentic Applications (2026, published December 2025). Typical mappings for this architecture include prompt injection and vector weaknesses in data operations, excessive agency and tool misuse in the assistant, privilege abuse in identities, and human-agent trust exploitation on the approval step.
Get trustworthy telemetry and a dashboard first, then a detector operations already believe, then a read-only explaining assistant, then draft work orders, and only then a tightly scoped, approved command path. Run a MAESTRO session before the first write tool goes live.
AWS · chatbot · guardrails
A customer-facing chatbot with flow control and guardrails kept apart. A deterministic engine owns intents, slots, validation and retries. A catalog of controls across six layers (input, context, model, tool and action, output, human oversight) screens every message in and every reply out, and a small Bedrock model is called only between questions, all on pay-per-use serverless services.
AWS and Azure AI
A grounded question-answering system for internal knowledge: governed ingestion, hybrid retrieval with permissions, a guarded model call, citations, an audit log and a repeatable evaluation harness.
AWS and Azure AI
An agent that can do things, not just answer: a bounded planner, a registry of narrowly scoped tools, policy checks outside the model, human approval for consequential actions and a full action ledger.
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