AI risk, rights & assurance
Data rights, output IP, privacy budgets, evaluation integrity, and agent action control.
24 of 388 tools.
Audit agentic action control integrity
Audit operational AI-agent actions from bounded least-privilege permission scope through independently tested authorization, approval, sandbox, monitoring, rollback or compensation, and kill-switch controls, counting unique value exposure once.
Audit AI configuration release integrity
Audit that the exact immutable AI configuration bundle evaluated and approved is the bundle exposed in every staged rollout, with consecutive parent lineage, complete blast-radius declaration, effective runtime controls, monotone traffic and a tested prior-version rollback path.
Audit AI data rights provenance integrity
Audit every AI training, fine-tuning, retrieval, evaluation, logging and persisted-output use against an immutable rights grant and the complete derivative lineage, including time, revocation, deletion, purpose, jurisdiction, consent, derivative and evidence gates.
Audit AI evaluation contamination integrity
Audit frozen AI evaluation suites for temporal or answer leakage, model-version mismatch, incomplete pre-label predictions, weak label provenance, missing subgroup support, cross-suite case reuse and near-duplicate content components before evaluation scores are trusted.
Audit AI output IP provenance integrity
Audit aggregate AI outputs against the exact model and provider terms effective at generation, pre-commercialization similarity evidence, counsel-owned ownership/use rules, human review and any claimed indemnity coverage.
Audit AI privacy budget integrity
Recompute each aggregate AI privacy account from its immutable release ledger using additive Rényi differential-privacy composition and target-delta conversion, while auditing order grids, sequence, hashes, accounting periods, purpose, review approval, evidence and claimed-versus-actual budget spend.
Audit AI regulatory obligation evidence integrity
Audit point-in-time AI-system classification, counsel-supplied obligation applicability, control evidence and incident-reporting clocks without pretending to infer law.
Audit shadow AI inventory integrity
Reconcile the approved AI-service registry against gateway, DNS/CASB, SSO, expense and provider evidence by deduplicating canonical aggregate usage events, then audit registration, status, domain/data-class policy, broker routing, contracts, security/privacy review, telemetry completeness and reported usage/spend.
Forecast agentic action loss
Forecast expected and tail operational AI-agent loss with tenant-local empirical-Bayes absorbing Markov chains across execution, deviation, containment, recovery, completion and loss, preserving shared control-failure regimes and unresolved-chain mass.
Forecast AI configuration regression loss
Forecast material AI configuration regression, rollback-capped request exposure, excess failures, net value and economic-loss VaR/CVaR from tenant-local concurrent control/candidate evidence, partially pooled lognormal severity, coherent operating scenarios and a shared platform-regression state.
Forecast AI data rights liability
Forecast correlated AI data-rights loss, disruption and response cost with locally calibrated Beta defect priors, partially shared Gaussian-copula occurrence and severity, lognormal harm, coherent jurisdiction scenarios, controls and portfolio VaR/CVaR.
Forecast AI evaluation production validity
Forecast whether offline AI evaluation scores will remain valid in production using a tenant-pooled Bayesian logit calibration with workload effects and time drift, coherent operating scenarios, false-promotion risk, breach timing, net value and quality-shortfall VaR/CVaR.
Forecast AI output IP claim liability
Forecast aggregate AI-output IP claim frequency, gross cost, defense and disruption, collectible indemnity and net VaR/CVaR using tenant-local Bayesian recurrence/severity evidence plus shared provider events and counterparty default.
Forecast AI privacy attack loss
Forecast correlated membership-inference or reconstruction loss from tenant-local member/nonmember red-team trials using Beta posterior attack advantage, partially pooled lognormal harm, binomial subject exposure, coherent attacker regimes, common asset-group compromise, control effects and loss VaR/CVaR.
Forecast AI regulatory change liability
Forecast counsel-defined regulatory-change frequency, lognormal remediation work and cost, capacity queues, shared jurisdiction shocks, enforcement exposure and liability CVaR.
Forecast shadow AI unseen exposure loss
Estimate AI services missed by every detector with a Bayesian zero-truncated binomial capture–recapture model, then simulate scenario-dependent visibility, incidents, common provider exposure, usage, value disruption and lognormal loss to produce unseen-inventory and economic VaR/CVaR tails.
Optimize agentic autonomy portfolio
Choose one manual, approval-required, bounded-autonomous or autonomous operating mode per action class, maximizing scenario net value under hard authorization/reversibility controls, shared-asset loss, reviewer capacity, cost, availability, dependencies and CVaR.
Optimize AI compliance control portfolio
Select reusable AI compliance controls and one plan per obligation using Beta-binomial residual risk, shared jurisdiction loss, exact shared costs/resources and a CVaR Pareto frontier.
Optimize AI configuration rollout portfolio
Select one current or staged rollout plan per AI configuration release, maximizing expected value minus CVaR regret under hard controls, failure ceilings, application concurrency, dependencies, budget and shared resources while computing overlapping application blast-radius loss once from joint survival.
Optimize AI data rights remediation portfolio
Choose license, replace, delete, disable or retrain actions that maximize preserved risk-adjusted AI value under budget, legal/execution gates, dependencies and scarce resources, while pricing scenario CVaR and counting shared lineage contamination once at its weakest residual member.
Optimize AI evaluation value of information portfolio
Select additional AI evaluation plans by multi-stratum posterior-predictive value of sample information, discounting duplicate content and optimizing budget, reviewer capacity, delay, quality lower bounds and expected/CVaR incremental false-deployment loss with exact or disclosed beam search.
Optimize AI output IP risk portfolio
Choose keep, scan, license, redesign, replace, exclude or insure policies per aggregate AI-output class using Beta-binomial claim simulation, collectible indemnity, unique provider loss, hard controls/resources and a CVaR Pareto frontier.
Optimize AI privacy utility portfolio
Select one validated privacy mechanism per AI workload to maximize expected value minus privacy-loss CVaR while enforcing exact shared-account RDP composition, utility, latency, controls, dependencies, exclusions, budget and scarce privacy-engineering capacity, with shared compromise priced once.
Optimize shadow AI governance portfolio
Choose block, broker, migrate, allow-with-controls or monitor policy for each aggregate shadow-AI service class, maximizing expected value minus loss CVaR under residual-exposure, detection, control, dependency, exclusion, budget and resource gates while pricing common provider value at risk once through joint survival.