Kythos AI · Cybersecurity and AI engineering
Secure the environment. Build the intelligence around it.
Kythos AI provides cybersecurity and AI engineering services. We assess customer-specific options for local, disconnected, and approved API-connected systems, then scope development and validation against the customer’s environment.
Kythos AI services
Security and AI engineering built around your environment.
We begin with the systems, data, permissions, and outcome that matter. Engagements may stand alone or be scoped into a customer-specific system and security program.
Cybersecurity
Authorized penetration testing
Human-led testing of named targets and permitted techniques, with written authorization, stop conditions, reproducible findings, remediation, and retesting.
Defensive engineering
Vulnerability and patch response
Discovery, triage, threat modeling, detection work, controlled malware analysis, remediation, candidate patches, and patch validation.
Kythos AI
Local and air-gapped AI
Explore a scoped assessment or development engagement for a local or disconnected AI system. Any model, connector, and AINN Fabric work is defined in the agreed statement of work.
Connected systems
Runtime connectors and assessment
Design a read-only first assessment using customer-approved APIs or connectors to inventory posture, collect evidence, identify gaps, and propose controlled actions.
Readiness
CMMC, NIST, endpoint, and MSSP support
Evidence preparation, control implementation, Tanium and endpoint workflows, remediation tracking, reporting, and verification.
Software
Automation and custom engineering
Internal tools, APIs, dashboards, installers, infrastructure automation, observability, and reliable deployment workflows.
Cyber operations depth
From attack surface to verified recovery.
Kythos AI works across the layers that determine whether a weakness becomes an incident. We connect technical findings to the identities, paths, controls, operating state, and recovery decisions that shape real exposure.
Architecture & exposure
Attack-surface and trust-path analysis
Define assets, entry points, identities, trust boundaries, data paths, administrative planes, inherited access, external dependencies, and high-consequence routes. Deliver an evidence-backed exposure map and prioritized validation plan.
Applications & code
Secure code and dependency engineering
Review source, build configuration, secrets handling, authentication, authorization, data flow, dependencies, unsafe interfaces, and deployment assumptions. Validate candidate fixes against the original condition and regression surface.
Identity & infrastructure
Privilege, endpoint, cloud, and platform review
Analyze effective permissions, service identities, role inheritance, endpoint state, management paths, network controls, cloud configuration, virtualization, Linux services, and administrative recovery access.
Detection & response
Telemetry and incident engineering
Map required evidence to available logs, identify blind spots, create detection logic and investigation paths, preserve timelines, test escalation, and document containment, eradication, recovery, and follow-up validation.
AI & automation
Agent, model, connector, and tool security
Threat-model prompts, retrieval, memory, model routes, runtime identities, tool permissions, secrets, MCP or API connections, approval gates, audit events, failure handling, and unintended action paths.
Delivery evidence
Findings that end in a verified change
Receive reproducible evidence, severity rationale, affected scope, mitigation options, patch or configuration work where contracted, rollback, acceptance tests, retest results, and clear ownership of remaining risk.
AI-assisted defensive security
Defensive analysis for authorized, permissioned work.
Kythos AI uses AI tools to help analyze code, configuration, logs, threat models, and proposed patches as part of a human-reviewed evidence chain.
Tooling is selected for each engagement according to authorization, provider terms, data handling, and the written scope. Naming a provider does not imply affiliation, partnership, endorsement, or certification.
Where AI can assist
- Vulnerability discovery and triage
- Code security review and threat modeling
- Detection engineering and incident response
- Malware analysis in controlled environments
- Remediation and patch validation
Lessons from defensive engineering
Defensive analysis looks beyond the alert to the surrounding system.
The most valuable lesson is that a vulnerability is rarely just one bad line. Defense improves when code, identity, deployment state, exposure time, telemetry, and recovery are analyzed together.
Major approach
Evidence-linked vulnerability analysis
Trace a finding from source or configuration through reachability, privileges, business impact, compensating controls, remediation, and retest. Every conclusion keeps its supporting artifact.
Major approach
Patch as a complete control
Review the diff, build path, dependency effects, regression surface, deployment sequence, rollback, telemetry, and the original reproduction test—not just whether the code compiles.
Often missed
Temporal exposure mapping
Look for short-lived privileges, stale tokens, race windows, delayed revocation, temporary firewall rules, and deployment gaps that disappear from steady-state scans.
Often missed
Negative-capability testing
Prove that an identity, connector, model tool, or recovery account cannot perform forbidden actions. A denied path is a security property worth testing and preserving.
Often missed
Control interaction failures
Test how individually correct controls combine: an allowlist plus inherited role, a patched service plus stale worker, or a safe tool plus overbroad runtime identity.
Often missed
Recovery-path equivalence
Verify that backup, break-glass, disaster recovery, and alternate deployment paths enforce the same security boundary as the primary system.
Multi-provider intelligence
Choose models and providers for the security boundary.
A scoped system design may use local models or customer-approved provider APIs where the implementation, provider terms, data-handling requirements, cost, and operating environment permit.
Customer-controlled routing
Choose local inference, a customer-owned API account, or an approved hybrid route according to data sensitivity, capability, cost, and availability.
Human-reviewed results
AI accelerates analysis and engineering. A human validates evidence, findings, changes, patches, and final deliverables.
Explicit authority
Every connector, scan, test, and change remains bounded by customer ownership, written authorization, provider terms, and an auditable operating path.
Cyber Knowledge
Judge the method, not the marketing.
Our knowledge library explains how we bound an environment, reproduce a condition, engineer a fix, and verify the result without exposing customer systems.
Evidence chain
- System boundary and authorization
- Reproducible evidence
- Mitigation, patch, or engineering change
- Rollback and acceptance criteria
- Retest, limitations, and remaining risk
Start with the environment in front of you
Bring one secure-AI requirement, security gap, or engineering backlog.
We will turn it into a written scope with boundaries, deployment choices, deliverables, and acceptance criteria.