AI Security
Agents introduce a threat class your security program hasn't met: software that reads untrusted text and might do what the text says. A hostile instruction hidden in an email, a document, or a scraped web page becomes an action executed with your agent's permissions. Prompt injection isn't a bug to patch — it's a property of the technology to architect around.
Our defenses assume the model can be fooled, because it can: least-privilege tool access so a compromised agent has minimal blast radius, permission enforcement in the execution layer rather than the prompt, output handling that treats model text as untrusted input, isolation boundaries between agents and the systems they touch, and audit trails that make every action attributable. Security review stops being the place AI projects die — it becomes the place ours get approved.
Prompt injection defense-in-depth
Input isolation, output constraints, and privilege boundaries — resilience by architecture, not by filter.
Agent least-privilege design
Scoped, time-bound, audited tool access — agents act as the user, never as the system.
Data leakage controls
Retrieval ACLs, output filtering, and tenant isolation that keep private data private.
AI red-teaming
Adversarial testing of your agents before someone hostile does it for free.
Security review acceleration
Threat models and control evidence that turn review from a blocker into a formality.
Is prompt injection actually exploitable or just a conference talk?
Actually exploitable, trivially, against naive systems — one crafted email can redirect an agent that reads inboxes and holds tools. The fix isn't a smarter filter; it's architecture: untrusted content never gets to drive privileged actions, and privileges are enforced outside the model.
Our security team keeps blocking AI launches. How do you help?
By giving them what nobody gives them: a real threat model, controls mapped to each risk, and evidence — audit logs, red-team results, permission matrices. Security teams block what they can't evaluate. We make our systems evaluable, and reviews move fast.
Can employees' AI tool sprawl be secured without banning everything?
Yes — bans just create shadow AI. The workable pattern is a governed gateway: approved tools with logging, data classification rules enforced at the boundary, and sanctioned alternatives good enough that people actually use them. Control through better options, not prohibition.
Put AI Security to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
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