Canada's specialist consultancy for agentic AI security, governance, and production architecture.
We help organizations assess, govern, and architect LLM and agentic systems before they are trusted with tools, data, workflows, or decisions.
Assessment
Attack surface review
Threat models, adversarial testing, privilege reviews, prompt-injection paths, tool misuse scenarios, memory risk, RAG exposure, and incident-ready findings for systems that can take action.
Governance
Operating control design
Inventory, risk classification, approval workflows, policy, vendor review, board reporting, human oversight, and accountability structures that fit real teams instead of shelfware.
Guardrails
Runtime boundary design
Data boundaries, PII handling, context controls, retention rules, eval gates, refusal behavior, monitoring, escalation paths, and human review for high-consequence AI workflows.
Systems
Production architecture
Evaluation harnesses, simulated environments, agent orchestration, identity, permissions, observability, scalable RAG, and production patterns for LLM systems that need to survive contact with users.
Generative AI produces outputs for people to review. Agentic AI acts across tools, data, and workflows. That shift changes the security model: access control, observability, reversibility, governance, and architecture become first-order concerns.
Over-broad agent authority
Agents often inherit broad credentials, service accounts, or human-like access. A single compromised workflow can move across systems faster than manual review can follow.
Prompt-to-action escalation
Prompt injection becomes more serious when the model can call tools, modify records, send messages, execute code, or trigger downstream automations.
Context, memory, and retrieval leakage
RAG, memory, logs, traces, and context windows can expose sensitive information unless privacy and retention controls are designed into the architecture.
Unclear evidence and reversibility
When agents plan, retry, delegate, and act autonomously, teams need evidence of what happened, why it happened, who approved it, and how it can be reversed.
Policies alone do not secure agentic systems. We connect board expectations, privacy obligations, security controls, and engineering reality so the operating model can be audited, tested, and improved as the system changes.
Assessment packet
What you leave with is evidence, not ceremony.
A prioritized register of AI-specific risks across agents, tools, permissions, prompts, retrieval, memory, data flows, vendors, and operational controls.
A practical comparison between the controls your deployment needs and the controls actually present in engineering, security, privacy, and governance workflows.
Clear risk language for decision-makers: what the system can do, what can go wrong, what is being controlled, and what should not be deployed yet.
Concrete architecture, governance, eval, monitoring, policy, and remediation steps that your teams can execute without translating a generic framework.
“Agentic systems should earn autonomy through evidence, controls, and reversible operations.”
We work at the boundary between security assessment, governance design, and production engineering. The goal is not to slow AI adoption. The goal is to make autonomy explicit, measured, permissioned, and accountable.
Our engagements produce board-readable risk language and implementation details your technical teams can actually ship.
Discovery
Trace the agents, tools, data stores, permissions, prompts, vendors, logs, and decisions that make up the system.
System inventory
Adversarial
Run adversarial scenarios against realistic workflows, including indirect prompt injection, privilege abuse, data exposure, and unsafe tool use.
Failure paths
Control design
Define ownership, approval paths, policy, evidence, review thresholds, and escalation rules that match the system's actual risk.
Operating model
Architecture
Refactor the architecture, guardrails, eval harnesses, monitoring, and release gates so controls become part of how the system ships.
Remediation plan
We do not publish client names, system details, or exploitable findings. Technical depth can be shared privately when the conversation warrants it and confidentiality is in place.
Evaluation infrastructure
LLM-based judging, human-in-the-loop verification, reviewer calibration, disagreement handling, and scalable quality control for agent behavior under realistic task conditions.
Regulated systems
Evaluation and deployment harnesses hardened for auditability, repeatability, evidence capture, release gates, access control, and controlled production rollout.
Model behavior
Fine-tuning workflows paired with jailbreak, refusal, policy-boundary, and regression audits to measure whether model behavior improves under adversarial pressure.
Guardrails
Guardrail systems for high-consequence interactions, including crisis detection, escalation logic, human review paths, privacy boundaries, and failure-mode monitoring.
MCP security
Red-team level engagement across MCP servers, tool manifests, delegated permissions, token exposure, prompt-to-tool escalation, and cross-system abuse paths.
Governance infrastructure
Organization-wide mapping of LLM and agentic systems by data access, tool authority, model and vendor dependency, user exposure, business criticality, and required controls.
AI development policy
Practical rules for teams using coding agents and rapid prototyping workflows, including approved tool use, repository access, review gates, secrets handling, provenance, and production handoff.
ARL is built for narrow, high-consequence work where engineering judgment matters more than headcount.
We are a very small but senior team. The people you speak with are the people doing the assessment, design, and implementation work.
We are principal and staff engineers as well as senior product leaders. Recommendations are written for teams that have to ship, operate, and defend real systems.
Agentic systems, evals, harnesses, MCP, guardrails, and AI-assisted development are moving quickly. We stay near the edge because the risks are changing there first.
Not every business needs us. We reserve time for organizations where autonomy, access, privacy, security, or governance materially changes the risk profile.
We are useful when AI systems carry real authority, exposure, or operational consequence. We are not useful for every AI project.
You send the system context, deployment stage, risk concern, and timeline.
We review whether ARL is the right team and say clearly if we are not.
If there is a fit, we define the assessment surface, access needed, deliverables, timeline, and confidentiality terms.
We run the work, brief decision-makers, and leave your team with findings, controls, and implementation steps.
Tell us what you are deploying, buying, governing, or trying to secure. We will review the context and respond with next steps.
ARL is best suited for teams working with LLM systems that have real access to data, tools, workflows, or decisions.
Every inquiry gets a response. We only take on work where we're the right fit, and we'll say so clearly when we're not.