Agentic AI & Security
Autonomous agents that stay secure and accountable
We design and deploy agentic systems that reason, plan, and act, and we secure and govern them so their autonomy stays auditable and safe. As AI takes on more decisions, the agent itself becomes part of your attack surface and your compliance obligations, so we engineer for both from the start.
Agentic AI
Agents engineered to survive production
Most agent pilots stall because no one designed for what happens when an agent retries, delegates, or selects the wrong tool. We design for exactly those conditions, so agents stay reliable as they take on real work.
Orchestration-first
Modular agents you can swap, extend, or replace without rebuilding the stack, coordinated by an orchestration layer that keeps complex, multi-step workflows coherent.
Tool use and action
Agents that act through your systems and tools under explicit, scoped permissions, so their capability never outruns your control.
Observability and guardrails
Trace-level observability, continuous evaluation, and guardrails, so failures surface early and behaviour stays within the bounds you define.
AI security & governance
Governance as a capability, not a compliance afterthought
Securing and governing AI is an engineering discipline. We treat agents as first-class identities and hold every action to least-privilege access, full auditability, and continuous monitoring, aligned with recognized frameworks.
Agents as identities
Every agent gets a scoped identity and least-privilege access by default, so it can do its job and nothing more.
Audit and monitoring
Every decision and tool-call is logged for audit, with continuous monitoring in production so anomalies are caught, not discovered after the fact.
Governance and ethics
Clear policies, bias and safety evaluation, and alignment with recognized governance frameworks such as Forrester’s AEGIS, so your AI stays defensible to regulators and boards.
Frameworks we align with
Designed against the standards your auditors recognize, on both sides of the border
International and industry
ISO/IEC 42001
AI management systems
OWASP Top 10 for LLM Applications
Application-layer threats
MITRE ATLAS
Adversarial tactics against AI
Forrester AEGIS
Enterprise AI governance
United States
NIST AI RMF and Generative AI Profile
Govern, map, measure, manage (NIST AI 100-1, 600-1)
CISA and NSA secure AI deployment guidance
Joint guidance co-sealed by the Canadian Centre for Cyber Security
SOC 2 Trust Services Criteria
What US enterprise procurement asks of vendors
NIST SP 800-171 and CMMC
Defence supply-chain requirements
Canada
Directive on Automated Decision-Making
Government of Canada
Voluntary Code of Conduct on Advanced Generative AI
Innovation, Science and Economic Development Canada
PIPEDA and Quebec Law 25
Privacy obligations AI systems inherit
Alignment describes how we design and document controls against these frameworks and laws. It is not a certification, attestation, or authorization claim.
Controls we engineer
Six controls that close the agent attack surface
An agent that reads documents, calls tools, and remembers context is a new class of system to defend. These are the controls we build into every deployment, mapped to the threats they close and the framework entries they satisfy.
- Threat
Prompt injection
Instructions smuggled in through documents, web pages, or tool results
Control we engineerUntrusted content is separated from instructions, tool outputs are handled as data, and injection attempts are detected and logged rather than silently followed.
ReferenceOWASP LLM01MITRE ATLASNIST AI RMF · Manage - Threat
Data exfiltration through tools
An agent steered into sending what it can read somewhere it should not
Control we engineerAgents reach data and tools only through scoped, allow-listed connectors with egress controls, so an agent cannot be steered into leaking what it is permitted to read.
ReferenceOWASP LLM02 · LLM06NIST AI RMF · MapSOC 2 · Confidentiality - Threat
Model supply-chain compromise
Tampered weights, poisoned data, or a swapped dependency
Control we engineerWeights, datasets, and dependencies are pinned, hashed, and provenance-tracked, so what runs in production is exactly what was evaluated.
ReferenceOWASP LLM03 · LLM04ISO/IEC 42001NIST SP 800-171 - Threat
Jailbreaks and unsafe actions
Behaviour that only appears under adversarial pressure
Control we engineerBefore release and on every change, agents are adversarially tested against jailbreaks, injection, and unsafe-action scenarios, with results held to a threshold you set.
ReferenceMITRE ATLASNIST AI 600-1NIST AI RMF · Measure - Threat
Credential and tenant leakage
Secrets in prompts, memory shared across customers
Control we engineerPer-tenant boundaries for data, memory, and credentials. Secrets never enter a prompt and are never available to a model.
ReferenceOWASP LLM02 · LLM07SOC 2 · SecurityPIPEDA · Law 25 - Threat
Runaway or compromised agent
An incident that spreads before anyone can stop it
Control we engineerEvery agent can be paused, rolled back, or revoked in seconds, with runbooks and alerting so an incident is contained rather than discovered.
ReferenceOWASP LLM06 · LLM10CISA/NSA guidanceDirective on ADM
References are to the OWASP Top 10 for LLM Applications (2025), MITRE ATLAS, the NIST AI Risk Management Framework and its Generative AI Profile, ISO/IEC 42001, SOC 2 Trust Services Criteria, NIST SP 800-171, the CISA and NSA guidance on deploying AI systems securely, and the Government of Canada Directive on Automated Decision-Making.
Work with us
Deploy autonomy you can govern.
We build the agents and the controls together: scoped identities, full audit, and continuous monitoring from day one.
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