Shadow AI Discovery & Governance
The Challenge
Your employees are using AI tools you don't know exist. Developers are calling external LLM APIs. Vendors are processing your data through third-party AI systems. Much of it has not been reviewed or tied to policy.
The CyberArmor.AI Answer
CyberArmor.AI uses supported endpoint, browser, API, and integration signals to surface AI usage, connect it to policy, and preserve evidence as coverage expands with each deployment path.
Key Outcomes
- AI asset inventory built from supported discovery signals
- Unreviewed AI usage surfaced for governance review
- Policy-based responses tied to deployment control points
- Audit-ready records for monitored AI usage events
AI Agent Trust & Control
The Challenge
Autonomous AI agents are being deployed to automate decisions, access systems, and orchestrate workflows. Without identity controls, behavioral bounds, and trust verification, these agents represent a new and largely unmanaged attack surface.
The CyberArmor.AI Answer
CyberArmor.AI gives agent workflows an identity and evidence model: registration, tenant scope, allowed tools, owner metadata, delegation context, revocation paths, and control outcomes.
Key Outcomes
- Agent identity verification before runtime execution
- Behavioral scope enforcement and anomaly detection
- Trust and delegation records for monitored agent actions
- Blast radius limitation and automated containment
Prompt Injection & Misuse Defense
The Challenge
AI chatbots and LLM-powered applications are being actively targeted by adversarial inputs designed to extract data, bypass controls, or manipulate model behavior. Traditional WAFs and input sanitization don't understand the semantics of AI prompts.
The CyberArmor.AI Answer
CyberArmor.AI provides runtime detection for adversarial prompts, jailbreak patterns, and indirect injection attempts, then connects the result to monitor, warn, block, or evidence outcomes depending on the approved control point.
Key Outcomes
- Real-time prompt classification and threat scoring
- Blocking or warning of adversarial inputs where enforcement is deployed
- Structured evidence for monitored injection attempts
- Coverage for direct and indirect prompt injection vectors
Sensitive Data Protection in AI Workflows
The Challenge
AI systems are receiving credentials, API keys, payment data, bank details, PII, trade secrets, and regulated data before traditional DLP or review workflows can intervene.
The CyberArmor.AI Answer
CyberArmor.AI can inspect AI-bound content and apply optional redaction-mode responses for supported secrets, PII, PCI, NACHA, NPI, and non-public indicators while preserving evidence without raw secret previews.
Key Outcomes
- Real-time data classification in AI interactions
- Credential and secret leak detection with redaction-mode policy outcomes
- Policy-based data handling enforcement per AI system
- Reduction of unauthorized PII or regulated data exposure in supported paths
- Compliance-ready evidence for AI data processing activities
Governed Enterprise AI Adoption
The Challenge
Business teams want to move fast on AI. Security and legal are blocking initiatives because there is no technical framework for safe, accountable AI deployment at enterprise scale. The result is either delayed value or ungoverned risk.
The CyberArmor.AI Answer
CyberArmor.AI provides the technical governance infrastructure for safe AI adoption: runtime policy, provider control, redaction, identity context, audit trails, and evidence built into the AI adoption lifecycle.
Key Outcomes
- Security policy framework for AI deployment programs
- Automated enforcement that doesn't slow delivery teams
- Audit trails for AI system approvals and usage
- Accelerated security review cycles for AI initiatives
Evidence-Based AI Incident Investigation
The Challenge
When an AI-related security incident occurs — a data leak through a chatbot, a compromised agent, a prompt injection that succeeded — security teams have no structured forensic evidence. Reconstructing what happened is expensive, slow, and incomplete.
The CyberArmor.AI Answer
CyberArmor.AI captures decision-level telemetry and action context across monitored AI paths, creating a structured, reviewable record that helps teams reconstruct actor, policy, data, provider, response, and evidence lineage.
Key Outcomes
- Decision-level telemetry across monitored AI interactions
- Structured incident timeline reconstruction in minutes
- Evidence-backed root cause analysis for AI incidents
- Legally defensible documentation for regulatory response
Continuous AI Runtime Monitoring
The Challenge
AI systems change behavior over time as models are updated, fine-tuned, or retrained. Without continuous runtime monitoring, drift from expected behavior — or adversarial manipulation — can go undetected for extended periods.
The CyberArmor.AI Answer
CyberArmor.AI provides runtime monitoring of AI systems and agents in supported paths, detecting anomalies, policy drift, and behavioral changes that can trigger investigation or containment workflows.
Key Outcomes
- Behavioral baselines for monitored AI systems
- Anomaly detection for drift, manipulation, and misuse
- Alerting with structured investigation context
- Integration with existing SOC workflows and SIEM platforms
AI-Aware Identity & Access Control
The Challenge
Traditional IAM was built for human users. Enterprise AI environments include services, workloads, and AI agents that all need access — but don't fit existing identity models. The result is over-provisioned, unmonitored non-human access.
The CyberArmor.AI Answer
CyberArmor.AI extends identity-aware access control to AI actors: users, services, workloads, and agents, with trust decisions that account for context, risk level, and behavioral history where those signals are available.
Key Outcomes
- Unified identity policy spanning human and non-human actors
- Context-aware trust decisions at time of access
- Least-privilege enforcement for AI services and agents
- Integration with existing IAM and zero trust frameworks