EventsSecure.com at Black Hat USA 2026

Product overview — Self-hardened cyber defense, powered by offense. Run by AI Teammates.

Four AI Teammates run your security operations over one live model of your environment — proving what is actually exploitable, clearing the queue it creates, and bringing you only the decisions that need a human. They take on the grunt work that scales with alert volume, so your team gets 24/7 coverage without a night shift. For lean teams facing enterprise threats without enterprise headcount.

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Proven by attack. Gated by approval. Above the stack you already own.

You get a queue ranked by what an attacker actually reached, automation you can switch on without losing control, your team’s time back — and a rollout that changes nothing you already run.

  • 01 / 04

    Defense Powered By Offense

    Your defensive queue is ranked by what an attacker actually reached — not by a severity label someone assigned.

    How it works
    • Red Teaming runs a four-phase chain against authorised targets: recon, vulnerability research, exploit, attack chain.
    • Validated exploitability feeds the risk score alongside KEV and EPSS intelligence, asset criticality and attack-path contribution.
    • Blast radius is proof-weighted — only steps that provably succeeded count, and reaching one crown jewel outranks touching fifty low-value hosts.
  • 02 / 04

    Governed AI Teammates

    Teammates that genuinely act — inside guardrails you can inspect, with no trusted mode that skips the gate.

    How it works
    • Every executable intent is tiered L1, L2 or L3; an L3 action is refused from the confirm path until approval is explicitly granted.
    • The platform is extract-only. Effect leaves as a ticket reference or a workflow run your environment authorised — never a silent write.
    • Every intent is logged before and after confirmation, and every score carries the evidence breakdown behind it.
  • 03 / 04

    No More Burn Out, No More Grunt Work

    The work that scales with alert volume stops being a person’s job, and 24/7 coverage stops needing a night shift.

    How it works
    • Duplicate alerts collapse on arrival; enrichment and a first-pass verdict are written before you open the case.
    • Ownership resolves automatically and the SLA clock starts itself. Accepted risk carries an expiry and reverts on its own.
    • Approvals reach Slack, Teams and email — a line manager, finance or legal unblocks the work without ever logging in.
  • 04 / 04

    Adoption Without Disruption

    Your stack stays exactly where it is. One agentless connection lights up every module at once.

    How it works
    • Agentless onboarding for AWS, Azure, GCP and Kubernetes — first value in hours, with no agent fleet to roll out.
    • 200+ integrations normalise to one common schema, so a new scanner onboards without reshaping anything downstream.
    • Start with one teammate and add the rest against the same context — and there is no per-connector charge anywhere in the model.

Connect. Build context. Work the queue — with you in the loop.

First value in hours, not a quarter. One agentless connection starts the collectors, the model builds itself from what comes in, and by the time the teammates pick up the queue it is already ranked — with nothing touching your environment until you approve it.

  1. Step 1

    Connect what you already run

    Agentless cloud onboarding plus 200+ integrations. Credentials go to a per-tenant vault; collectors start pulling immediately. The dashboard tells you honestly whether it is empty, syncing or complete.

    • Hours, not a rollout project
    • No agent fleet to deploy
    • No per-connector line item
  2. Step 2

    Everything lands in one live model

    Signals normalise to a common schema, then bind to assets, identities, owners, topology, controls and business criticality. That live model is what makes correlation — and attack paths — possible at all.

    • Assets, identities, exposures, topology
    • Attack paths, controls, cases, risks, owners
    • Snapshots you can travel back through
  3. Step 3

    Teammates work it; you approve

    Four specialised AI Teammates triage, investigate, validate, prioritise and prepare the fix. Work surfaces where you already are — Slack, Teams, email, your ticket queue. Consequential actions wait for a person.

    • Approvals reach people who never log in
    • Effect leaves as a ticket or an authorised workflow run
    • Evidence accumulates as a by-product

Follow the work down the stack.

Five layers, read top to bottom. Your tools feed a shared context model, product modules reason over it, the AI Teammates orchestrate those modules, and nothing reaches your environment without a person approving it.

01Your existing stack

Stays exactly where it is

  • Cloud

    AWS, Azure, GCP, Kubernetes.

  • Detection

    SIEM, EDR, gateways, WAF, firewall.

  • Identity

    IdP, directory, MFA, privilege.

  • Scanners

    Vulnerability, CSPM, external exposure.

  • Code & delivery

    GitHub, GitLab, CI/CD, SAST, SCA, IaC.

  • Offensive results

    Red Teaming runs and exploit proofs.

  • Work systems

    Jira, ServiceNow, Slack, Teams.

  • Endpoint

    EDR and host-level telemetry.

02The shared context model

One live model of your estate

How context gets built

Telemetry normalises to a common schema, then binds to assets and identities. Exposures, topology, attack paths, controls, cases and risks stay linked to each other rather than sitting in separate tools.

  • assets
  • identities
  • exposures
  • topology
  • attack paths
  • vulnerabilities
  • controls
  • cases
  • risks
  • owners

Time travel

Posture is kept as history. Step back through snapshots to see how assets, risks and paths looked on a given day — enforced read-only at the query layer, so reviewing the past can never change the present.

  • daily risk delta
  • posture history
  • read-only by design

03Product modules

Grouped by the job they do
Foundation
  • Asset Insight
  • Data Pipelines
Find
  • Vulnerability Management
  • Misconfiguration Management
  • Network Exposure
  • Application Security
  • Attack Path & Correlation
  • Identity Security
Decide
  • Risk Register
  • Case Management
  • Framework Mapping & Evidence
Act
  • Remediation
  • Secure Workflows

04AI Teammates

Outcome owners

  • Red Teaming

    Can an attacker really do this, and how far would they get.

  • SOC Teammate

    What happened overnight, and what needs a human.

  • CSPM Teammate

    Is the cloud configured safely, and what is actually reachable.

  • AppSec Teammate

    Will the code we are shipping hurt us in production.

Buy the teammate you need most; the rest stay hidden until you add them. Risk and asset intelligence are platform capabilities — every teammate uses them, none is billed separately for it.

05You and your teammate

Where the work reaches you

  • Chat & canvas

    Ask in plain language across the product.

  • Slack & Teams

    Decide from the channel you already live in.

  • Email approvals

    Approve with no login at all.

  • Your ticket queue

    Jira and ServiceNow, kept in sync.

  • Role dashboards

    CISO, manager, L1/L2/L3.

Four teammates. One shared context. One conversation.

Each teammate owns a question, not a tool. They read the same model and hand work to each other, so an offensive finding lands in the defensive queue without anyone re-typing it. Buy the one you need first; the others switch on when you are ready.

“Can an attacker actually do this, and how far would they get?”

Recon → Vuln research → Exploit → Attack chain

  • exploit validation
  • ATT&CK tagging
  • blast radius
  • scoped rules of engagement

Runs authorised offense against real targets and produces proof, not probability. Scope is versioned and fail-closed — any host outside the whitelist blocks the call — with a mid-run kill switch you control. Engagements are scoped per target, and the same ground is re-tested after remediation.

The teammate who’s already in your Slack

An AI Teammate is someone you ask, not a console you operate. Ask what changed overnight, why a finding matters, or what to fix first — and you get a straight answer with its working shown, in plain language.

They come to you, too. An AI Teammate raises what needs you in Slack, Teams or email, answers your follow-up questions in the same thread, and takes your approval right there. The work happens where your team already is — not in a console someone has to remember to open.

You name yours during onboarding. It is your teammate, not our mascot.

  • ask in plain language
  • Slack
  • Teams
  • email
  • answers in-thread
  • approve from the channel

Offense doesn’t just find things. It re-ranks everything behind it.

Most tools tell you what could be wrong. Secure.com proves what is wrong, fixes it, then attacks the same ground again to check the fix held. Four steps, and it never stops going round.

OFFENSEProve itRed Teaming runs theattack, keeps what worked.DEFENSERe-rank itProven exposure jumpsthe queue. Theory sinks.DEFENSEFix itThe change that breaksmost chains — you approve.OFFENSERe-test itSame ground, attackedagain, to prove it held.and the next attack starts harder
  • Step 1 · Offense

    Prove the path

    Red Teaming runs the four-phase chain against authorised targets inside versioned rules of engagement, and every action is tagged to a MITRE ATT&CK technique. What comes back carries a proof, not a probability score.

  • Step 2 · Defense

    Re-rank the queue

    Validated exploitability feeds the risk score alongside asset criticality, KEV and exploit intelligence, and attack-path contribution. Proven paths jump the queue; theoretical findings sink.

  • Step 3 · Defense

    Fix what breaks the most

    The change that breaks the greatest number of chains is proposed with its blast radius attached, approved by a human, and executed as a ticket or an authorised run.

  • Step 4 · Offense

    Attack it again

    The same ground is tested again to prove the exposure is actually gone. Nothing is assumed closed because someone marked it closed.

Why it compounds: prioritisation stops being an argument about severity labels and becomes an argument about evidence — an adversary already tried this, here is how far it got, here is the one change that breaks the chain. Each pass raises the floor, so the next attack starts harder.

Where your data sits, and what a teammate is allowed to do.

Your data stays inside your own boundary, it never trains anything shared, and nothing runs in your environment without your say-so.

  • Your tenant is genuinely separate

    Your own database, your own identity realm, your own credentials — not a shared pool with a filter on top. Another customer’s query has nowhere to reach your data from.

  • Your data never trains a broader intelligence

    Never pooled with other customers’. Never used to train shared or foundation models. The teammates reason over your context — they do not absorb it.

  • Nothing runs unless you allow it

    Reading and explaining just happens. Anything that changes your environment waits for your approval, and no mode skips that step. The platform never writes into your tools directly — it produces a ticket or a run you authorised, logged before and after.

Let us prove it
against your environment.

Bring the outcome you care about most — triage, cloud posture, code risk, or exposure validation — and we will show you the path from your telemetry to a fix your team approved. Or skip the slides: point Red Teaming at one real target and read the proof.

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