ASSURANCE & SECURITY
As AI systems become more autonomous, the governance architecture has to keep pace. We build the evaluations, guardrails, monitoring, and assurance processes that keep your systems trustworthy in production. Grounded in frontier alignment research, tailored to what your system actually does.
WHAT WE DELIVER
Every engagement produces governance architecture specific to your system's risk surface. Here's what that looks like in practice.
Behavioral test suites designed for your system's specific deployment context. What does your system do? What could go wrong? We build the evals that answer those questions before your users encounter them. Covers hallucination, instruction following, boundary adherence, and domain-specific failure modes. Updated as your system changes.
Adversarial testing against the failure modes your system is most exposed to. Prompt injection at every untrusted content ingress. Multi-turn escalation attacks. Social engineering probes. Tool-chain exploitation. We try to break your system the way a motivated adversary would, then fix what we find.
The operational controls that run in production alongside your system. Least-privilege tool scoping. Input filtering for injection. Output monitoring for fabrication and data leakage. Human-in-the-loop gates on irreversible actions. Per-call authorization. Configured to your system's specific risk surface and updated as autonomy changes.
Immutable, out-of-band logging with no model write access. The single highest-leverage control against oversight tampering. Every tool call traced. Every recommendation auditable. Anomaly detection on behavior patterns. Designed to survive a hypothetical agent compromise.
Controls on what the system can see, say, and share. Citation requirements on every claim. Dual-artifact patterns (full output for leadership, sanitized output for broader teams). Privacy controls on data access. Role-based visibility. Anti-hallucination architecture built into the output format.
Multi-model review, output verification against source material, cross-checking, and regression testing across deployment cycles. Automated QC that catches degradation before it reaches users. Tracks whether the system's own recommendations led to the predicted outcomes.
Who can see what. Visibility scoped at the data level, not bolted on at query time. Per-user, per-role, and per-project access controls. Audit trails on every query. Designed so expanding the system's capabilities doesn't expand its access surface.
Governance is not a one-time audit. As capabilities advance, the risk surface changes. We track developments in alignment research, update evaluations and guardrails accordingly, and re-assess your system's posture as it evolves. The governance layer stays current with what the system can do.
HOW IT SCALES
The governance architecture scales with your system's complexity and autonomy. A simple assistant needs different controls than a multi-agent strategic system. We scope the work to what your system actually requires.
Fabrication, injection, sycophancy. Standard eval suite and input filtering. Well-understood engineering.
Add scope violation risks. Least-privilege scoping and HITL gates are the highest-leverage interventions.
Add context degradation and selective disclosure. Monitoring for correlated failures that suggest a shared root cause.
Add inter-agent coordination risks. Competitive dynamics can degrade alignment even with explicit assurance instructions.
The full risk surface applies. This is where governance matters most and where our alignment research is most directly relevant.
THE DEPTH BEHIND IT
Every governance decision we make traces to a comprehensive diagnostic framework that maps AI failure using a structure borrowed from medical diagnostics. Observable symptoms trace to underlying mechanisms. Mechanisms have specific treatments, partial mitigations, or containment strategies. Adversarial attack categories map to the mechanisms they exploit.
We map what's observed in production today, what's been demonstrated in labs, and what's theoretically expected. The continuum between alignment research and production engineering is closing. Failure modes that were lab curiosities twelve months ago are now production incidents.
This framework is what makes the governance concrete. When we say "your system needs monitoring for information shaping," we can point to the specific mechanisms that produce it, the specific conditions under which it emerges, and the specific mitigations that are and aren't effective. The depth is there when you need it.
See the full diagnostic framework →THE RESEARCH
AE runs an active alignment research program alongside production client work. The research wins DARPA contracts and Anthropic partnerships. The findings go directly into the governance architecture we build for clients.
Representation-level alignment. Up to 97% reduction in deceptive responses.
Targeted capability removal without degrading general performance.
Self-monitoring: models that detect and resist misuse of their own capabilities.
Grant-funded alignment research sprints in partnership with frontier researchers.
Every capability advance changes the risk surface. The governance architecture has to advance in step. AE tracks every major development in alignment research, updates the diagnostic framework, and feeds the findings into the systems we build and maintain. This is not a one-time audit. It is a continuous practice.
Talk to us about alignment