Alice: AI Security and Safety Controls for Government Operations
Securing the AI Applications and Agents Government Agencies Deploy
As federal, state, and local agencies deploy AI into mission-critical workflows, those systems introduce new failure modes with direct operational consequences such as prompt abuse, policy bypass, model manipulation, and behavioral drift. A wrong model response in a law enforcement tool, benefits determination system, or intelligence workflow represents mission failure.
Alice gives agencies the controls to prevent it.
About Alice
Alice (formerly ActiveFence) is a safety, and security company purpose-built for the AI era. Its WonderSuite platform covers the full AI application lifecycle in a single environment: adversarial stress-testing before deployment, custom-policy-enforced guardrails at runtime, and continuous re-testing in production.
Alice is model-agnostic, protecting every model and agent in your environment with guardrails calibrated to your agency's policies rather than generic benchmarks.
Detection is powered by Rabbit Hole, Alice's adversarial intelligence engine built from a decade of real-world attack data across billions of users. Not synthetic red-teaming. Actual adversarial patterns drawn from production environments at scale.
Why Alice for Government?
- Pre-deployment testing that finds what lab benchmarks miss. Alice red-teams AI applications against real adversarial patterns before they go live, surfacing vulnerabilities under the conditions attackers actually use.
- Runtime guardrails that hold under pressure. Policy-aligned enforcement runs at ultra-low latency (sub-99ms), keeping AI systems within defined behavioral boundaries without degrading performance for end users.
- Continuous production monitoring. AI models drift. Alice detects behavioral changes in deployed systems before they become incidents, with alerts tied to the specific policies and standards your agency operates under.
- One audit trail across your AI portfolio. Every test, violation, and remediation is logged, mapped to NIST AI RMF, OWASP, ISO 42001, and other applicable frameworks, giving compliance teams the documentation they need.
- Coverage built for operational environments. Multimodal and multilingual support means Alice works across the full range of inputs government AI systems handle, including text, voice, images, and non-English content.
Use Cases
Alice supports agencies across a range of high-stakes AI deployments:
- Adjudication and benefits determination systems where model outputs carry legal weight
- Law enforcement and investigative tools where behavioral drift creates liability
- Citizen-facing AI services where a wrong input or response causes direct harm
- Internal automation and agent workflows where policy bypass creates compliance exposure
- Intelligence analysis platforms where adversarial manipulation is an active threat vector
The WonderSuite Platform
Alice's WonderSuite runs three functions in a unified platform:
WonderBuild: Pre-deployment red-teaming and adversarial stress-testing. Identifies how your AI system fails before operators and adversaries find out in the field.
WonderFence: Real-time, policy-aligned guardrails at inference. Enforces behavioral boundaries without adding latency that degrades mission workflows.
WonderCheck: Ongoing evaluation in production. Monitors deployed models for drift, new failure modes, and compliance gaps as systems and threat environments evolve.
Professional Services
Alice's in-house security research team and a global network of subject matter experts provide hands-on support for agencies that need more than platform access.
Adversarial Testing and Technical Investigations: Alice security researchers conduct deep investigations into account and API abuse, malware activity, scraping, and model distillation. Useful when an agency needs to understand a specific threat pattern rather than run a general assessment.
Red Teaming and Evaluation Baselines: Custom adversarial testing engagements that establish security baselines and validate system robustness against your agency's specific threat model and use cases.
AI Control Gap Assessments: A structured assessment that maps your stakeholder environment, identifies rollout blockers by use case, and surfaces control gaps in deployed or planned AI agents. Designed for agencies working through authorization or deployment approval processes.
Threat Intelligence: Curated alerts and custom reporting on adversarial activity targeting AI systems, including emerging malicious tools and newly identified model vulnerabilities. Relevant for agencies with a defensive mission or those operating AI systems in contested environments.
Compliance and Procurement Support: Specialist support for policy tuning and documentation required for procurement review and regulatory sign-off. Directly addresses the documentation burden that slows government AI deployment.
Data and Training Services
Agencies building or fine-tuning AI models for government-specific applications often can't source the training data they need from commercial datasets. Alice provides:
Training Datasets on Demand: Human-created, synthetically generated, or adversarially sourced datasets for supervised fine-tuning and RLHF. Tailored to your model's domain and safety requirements.
Expert Network Access: On-demand access to a global network of PhDs, policy experts, and native speakers across 100+ languages. Available to create specialized datasets for sensitive domains including CBRN safety, where standard training data doesn't exist or can't be used.
Novel Attack Generation: Alice security researchers develop custom sets of indirect prompt injection attacks tailored to a specific system's architecture and deployment context. Supports red team exercises and pre-deployment hardening for agencies that face sophisticated adversaries.
Getting Started
Government agencies don't get to choose whether AI succeeds or fails in their environment. They're responsible for the outcome either way. Alice gives your security, compliance, and mission teams a shared set of controls so that when something goes wrong in testing, it stays in testing.