Security
Security
Autonomy raises the stakes on security, so we build it into the substrate. Agents act under least-privilege identities, inside guardrails that define what they may never do, with every action logged to a tamper-evident record. Security here is not a review at the end — it is the reason the board can approve autonomy at all.
Outcomes
What you get, stated as results.
- Least-privilege identity and runtime guardrails on every agent
- A tamper-evident record ready for audit and regulators
- Security modelled for autonomy, not retrofitted
Capabilities
What this practice covers.
Agent identity & access
Every agent runs under a scoped, revocable identity with least-privilege access to systems and data.
Guardrails & policy
Hard limits on what an agent may do, enforced at runtime — not left to the model to respect.
Tamper-evident audit
An immutable record of every action, decision, and data touch, ready for regulators and internal audit.
Threat modelling for autonomy
We model the new attack surface autonomous systems create — prompt injection, tool abuse, data exfiltration.
Data protection
Masking, residency, and encryption applied where agents read and write sensitive data.
Continuous assurance
Ongoing monitoring and red-teaming of live agents, not a one-time sign-off.
How it works
A sequenced path, not a big bang.
Model the threats
Map the attack surface autonomy introduces across identity, tools, and data.
Set the guardrails
Runtime policy defining the actions no agent may ever take, enforced in code.
Instrument everything
Tamper-evident logging wired into every agent action and data touch.
Assure continuously
Live monitoring and red-teaming keep assurance current as agents change.
Where it applies
FAQ
Security — questions, answered.
How do you secure autonomous AI agents?
We build security into the substrate. Agents act under least-privilege, revocable identities, inside runtime guardrails that define what they may never do, with every action logged to a tamper-evident record. Security here is not a review at the end — it is the reason the board can approve autonomy at all.
What new security risks do autonomous agents introduce?
Autonomy creates a new attack surface: prompt injection, tool abuse, and data exfiltration. We model these threats across identity, tools, and data, then enforce hard limits at runtime rather than leaving the model to respect them, and red-team live agents continuously as they change.
Can you prove what an agent did for auditors and regulators?
Yes. Every action, decision, and data touch is written to an immutable, tamper-evident record — zero agent actions fall outside the audit trail. It is ready for regulators and internal audit, so you can replay exactly what an agent did and why, without reconstructing it after the fact.
Are guardrails enforced by the model or by the system?
By the system. Guardrails are hard limits on what an agent may do, enforced at runtime and in code, not left to the model to honour. That distinction is what lets least-privilege identity and policy hold even when a prompt tries to push an agent past its bounds.
Is security a one-time review or ongoing?
Ongoing. Continuous assurance means live monitoring and red-teaming of agents in production, not a one-time sign-off. Because agents change as workflows shift, assurance is kept current — threats are re-modelled, guardrails updated, and the audit trail reviewed rather than filed and forgotten.
Put Security to work.
Tell us the workflow. We'll show you the shortest path to a running agent.
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