Real Security Problems.
Real Enterprise Answers.

The Challenge

Regulators are moving fast on AI. The SEC and FINRA expect demonstrable cybersecurity controls, ISO/IEC 42001 sets the bar for AI management systems, and your board wants proof — not policy documents — that AI is governed. Manual evidence-gathering across disconnected tools doesn't survive an audit.

The CyberArmor.AI Answer

CyberArmor.AI maps enforceable controls to 17 framework policy packs — including ISO/IEC 42001, SEC Cyber, FINRA Cyber, and NYDFS 500 — and preserves tenant-scoped, persisted evidence bound to each control decision, so an assessment becomes a query instead of a fire drill.

Key Outcomes

• Control templates mapped to the frameworks your auditors already use
• Evidence bound to the enforcement decision that produced it
• Per-framework assessment reports, scored and stored
• Attestation-ready exports for auditors, regulators, and the board

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 surfaces AI usage from endpoint, browser, API, and integration signals, connects it to policy, and preserves evidence for every event it records — with an AI Bill of Materials inventory across endpoints, repositories, and cloud sources.

Key Outcomes

• AI asset inventory (A-BOM) across endpoints, repositories, and cloud sources
• Unreviewed AI usage surfaced for governance review
• Policy-based responses at each deployed control point
• Audit-ready records for AI usage events

The Challenge

Autonomous AI agents are being deployed to automate decisions, access systems, and orchestrate workflows. Without identity, tool, and trust controls, 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, allow-listed tools, owner metadata, delegation chains, suspension and token revocation, and audit evidence for every control decision.

Key Outcomes

• Agent identity registration and verification before runtime execution
• Allow-listed tools enforced per registered agent
• Delegation chains recorded from initiating user to acting agent
• Agent suspension and token revocation when trust is withdrawn
• Audit evidence bound to every agent control decision

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 detects adversarial prompts, jailbreak patterns, and indirect injection attempts at runtime, then applies the policy outcome — monitor, warn, block, or redact — and writes the evidence record.

Key Outcomes

• Real-time prompt classification and threat scoring
• Block, warn, or monitor outcomes for adversarial inputs
• Structured evidence for detected injection attempts
• Coverage for direct and indirect prompt injection vectors

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 inspects AI-bound content and applies redaction policy outcomes for secrets, PII, PCI, NACHA, NPI, and non-public indicators — enforced in the AI proxy and endpoint agent — while preserving evidence without raw secret previews.

Key Outcomes

• Real-time data classification in AI interactions
• Credential and secret leak detection with redaction policy outcomes
• Policy-based data handling enforcement per AI system
• PII and regulated data redacted or blocked before it reaches the provider
• Compliance-ready evidence for AI data processing activities

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

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 for every policy decision, creating a structured, reviewable record that reconstructs actor, policy, data, provider, response, and evidence lineage.

Key Outcomes

• Decision-level telemetry for every AI interaction the platform sees
• Structured incident timeline reconstruction in minutes
• Evidence-backed root cause analysis for AI incidents
• Legally defensible documentation for regulatory response

The Challenge

AI traffic changes constantly as models, tools, and usage patterns shift. Without continuous runtime monitoring, injection attempts, data leaks, and policy violations go unseen until an incident forces the question of what happened.

The CyberArmor.AI Answer

CyberArmor.AI monitors AI runtime activity through the policy engine, detection service, and audit pipeline: every decision produces telemetry, incidents open with structured context, and events forward to your SIEM.

Key Outcomes

• Decision-level telemetry for every AI interaction the platform processes
• Detection findings for prompt injection, sensitive data, and toxicity in live traffic
• Incidents opened with structured investigation context
• SIEM forwarding to Splunk, Sentinel, QRadar, Elastic, Google SecOps, and Syslog/CEF

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. Enterprise SSO (OIDC) with just-in-time provisioning and MFA secures the humans, while directory enrichment from Entra ID, Okta, Ping, and AWS IAM Identity Center resolves every audit event, action, and incident to a named user, department, and group.

Key Outcomes

• Enterprise SSO with JIT provisioning and multi-factor authentication
• Every event attributed to a real named identity, not an opaque ID
• Unified identity policy spanning human and non-human actors
• Registered agent identities with allow-listed tools and revocable credentials

The Challenge

AI runs on endpoints, and so do the vulnerabilities. Out-of-date browsers, Office apps, and third-party software are the soft underbelly of every fleet — and proving patch compliance across mixed Windows, macOS, and Linux estates is painful, manual, and usually spread across several tools.

The CyberArmor.AI Answer

The CyberArmor endpoint agent that carries AI governance to the device also remediates it: automated, policy-gated patching through native package managers, signature and hash-based malware detection, and a live inventory of what is outdated and exploitable — one agent instead of five.

Key Outcomes

• Cross-platform patch remediation with maintenance windows and approval gates
• Remediation prioritized by CVE, CISA KEV, and EPSS exploitability
• Signature and hash malware detection with a synced intelligence feed
• Patch-compliance evidence ready for audits