ai security for eval-native teams.
evalops exists because classic security playbooks expire every quarter. generative ai changes prompts, policies, and attack surfaces faster than ticket queues can keep up.
instead of bolt-on controls, we wire evaluations into every deployment decision. release contracts decide who can ship, probes watch what users experience, and audit trails document why leadership trusted the change.
the result is a safety program your ciso can defend in front of the board while product teams keep shipping.
guardrails the platform enforces
release contracts gate trust
evalops ties CI approvals, feature flags, and runtime entitlements to eval verdicts so jailbreaks and regressions never graduate from staging.
live probes stay on watch
automated agents replay prompt-injection, data exfiltration, and hallucination scenarios continuously, surfacing drift the moment it appears.
auditable evidence by default
verdicts, transcripts, and rollout rationales are preserved as immutable artifacts for auditors, boards, and regulators demanding proof of control.
aligning with the frameworks leaders cite
nist ai-rmf
map eval coverage to govern-map-measure-manage functions, generate compliance briefs per release, and keep risk registers aligned to real behavior.
mitre atlas
instrument evals against known adversary tactics so red-team findings translate into automated regression suites instead of slide decks.
owasp llm security
wire prompt validation, input sanitization, and supply-chain checks into the deployment pipeline so every mitigation ships with a detector.
plays i run with enterprise teams
catch prompt-injection pivots before launch
release contracts enforce isolation when probes spot role hijacking or cross-tenant bleed, keeping customer data sealed even under sustained attack.
keep model drift accountable
continuous evals compare new weights, retrieval corpora, and guard prompt sets against production baselines so policy changes are evidence-backed.
prove governance to executives
boards and regulators receive audit-ready summaries showing which controls fired, why exceptions were granted, and how quickly patches landed.
field notes and deeper dives
the security engineering series distills how i translate incident response muscles into model governance.
research logs cover cognitive dissonance detection, adversarial evaluation design, and pressure tests with real deployments.
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ready to see evalops against your stack?
send over your threat model, preferred frameworks, and the models in play. i will return a tailored evaluation plan and timeline.