A continuously learning cyber defense platform built around identity, trust relationships, attack paths, autonomous reasoning, and recovery-first security operations. Unlike traditional security products that correlate alerts after the fact, Logic Bounce continuously models enterprise reality and acts on that understanding in real time.
Every capability within the Platform is powered by a shared security graph and autonomous reasoning architecture. Rather than maintaining separate databases and isolated security tools, the platform continuously models enterprise reality through a unified operational understanding.
The Unified Telemetry Fabric ingests, normalizes, correlates, and enriches data from every security, identity, cloud, and AI source across the enterprise.
Convert disparate telemetry into a common operational model.
Identify relationships across users, assets, applications and identities.
Reconstruct complete attack timelines from distributed events into a single timeline.
Map human, machine, cloud and AI identities into a common identity model.
The Security Graph serves as the platform's continuously updated model of enterprise reality. It represents identities, resources, permissions, trust relationships, attack paths, and business context.
Continually model identities, assets, permssions, sessions, applications,Cloud Resources, AI Agents, Trust Relationships.
Continuously evaluate privilege Escalation, Lateral Movement,Credential Exposure and Trust Abuse.
When a compromise occurs "Who can be reached?", "What systems were exposed" and "What privileges were obtained?" are calculated instantly.
Trust relationships are continuously recalculated across User -> Saas, User -> Cloud, Agent -> API, Application -> Database
The Autonomous Reasoning Engine acts as the operational brain of the platform, continuously investigating, correlating, prioritizing, and explaining security events.
The Security Operations Brain. The reasoning engine continuously evaluates: Threats, Exposure, Behaivor, Risk and Trust instead of simply processing alerts.
Whe suspicious activity appears, the platform will automatically gather evidence, build timelines, identify root cause, determine blast radius and recommend response.
Creates a narrative which correlates across the dimensions of Identity, Endpoint, Cloud, Network, and AI activity
Generates attack narratives automatically, that take into account: Initial Access, Persistence, Privilege Escalation, Lateral Movement, Objectives.
Human-governed autonomy ensures that every automated decision remains aligned with business requirements, risk tolerance, governance policies, and trust models.
The Platform never operates without governance. Enable policies that define Allowed Actions, Approval Requirements, Risk Thresholds and Business Constraints
The Platform continuously evaluates Identity, Device, Session, Agent and Application Trust
A range of automations are available including "Auto Investigate", "Auto Enrich", "Auto Contain", "Notify Analyst", "Require Approval" and "Escalated to Leadership" that can be triggered depending on the risk level of the case.
Machine-speed response actions allow the platform to contain threats in seconds rather than hours.
The Platform can that actions in the event of an incident including: Endpoint, Session Termination, Account Disablement, Priviledge Reduction, Token Revocation, SaaS containment, AI Agent Suspension
The Platform continuously Detects, Investigates, Decides, Responds, Validates and Learns.
Recovery-first architecture ensures rapid restoration of trusted operational states after security incidents.
Traditional security asks "How do we stop attacks?" but a recovery first architecture ask "How quickly can we restore trust?"
The Platform recovers identities, workloads, cloud resources, endpoints and AI systems to a known-good state.
The Platform automatically validates integrity, restores configurations, rebuilds trust and confirms operational readiness.
Protect AI agents, autonomous workflows, MCP servers, machine identities, and LLM-powered business systems.
Protects AI Agents, LLM Applications, MCP Servers, Autonomous Workflows and Machine Identities by insituting Agent Identity Governance, Prompt Injection Defense, Runtime Monitoring, Tool Invocation Validation and Agent Trust Enforcement
Security teams are facing adversaries that increasingly leverage AI to search for vulnerabilities 24/7 and launch attacks at scale with nearly limitless variation. An autonomous SOC supplements security teams by providing tireless, around-the-clock alert triage, investigation, and response, leading to:
Shared Operational Understanding
Unified Investigations
Consistent Governance
Machine-speed operations
Using AI analytics to make connections between disparate intelligence signals
Reducing noise through automated alert triaging. Escalating only real critical alerts by clearing away false positives.
Using AI to identify and respond to emerging threats while freeing up human analysts for threat hunting.
Automating repetitive, time-consuming security operations processes and using AI to generate documentation, transform data, and quickly build workflows.
Incidents are resolved faster through the use of intelligent prioritization, AI-accelerated investigation and response, and contextual case enrichment.
With the ablity to translate natural language commands into technical actions allows junior analysts to operate at a higher level
Automating repetitive tasks to focus on more rewarding work.
Intelligently assigning case workloads by skill, experience and availability
By increasing efficiency, reducing operational overhead, and minimizing security breaches.
Move beyond fragmented security tooling and adopt a continuously learning cyber defense platform.