Every security vendor is an AI company now. Ask what their AI actually does, and most of them describe a copilot bolted onto a product that was built to hand you a ticket.
AI-automated phishing emails reached 54% click-through rates (opens in new tab), compared with 12% for standard phishing attempts. Phishing-initiated breaches (opens in new tab) took 254 days to identify and contain in 2025. The gap between how fast attacks move and how fast your tools respond is the whole problem.
The security stack is the set of point solutions most enterprises stitch together to cover that gap: one tool for endpoints, another for the cloud, another for data, and so on, each with its own vendor and console. The companies below didn't add a chatbot to the old tool in one of those layers. They replaced it. Each one owns a layer of the stack. Together, they're a new operating model for security.
Three things distinguish a replacement from a retrofit:
The companies below illustrate a shift toward continuous monitoring, automated action, and learning from resolved cases.
What you have now
Digital risk protection watching for lookalike domains, an email gateway scoring phishing, and a security-awareness platform running quarterly training. Three vendors, three consoles, no shared view of the same campaign.
What replaces it
Doppel unifies all three under one Threat Graph. It detects impersonation across domains, social media, ads, telco, and the inbox, dismantles the infrastructure behind it, and turns a live attack into an employee simulation with one click. No separate DRP, gateway, and training budget.
Founder on why the old way broke
"Legacy email security vendors are still optimizing for better scoring and faster quarantine."
— Kevin Tian (opens in new tab), CEO and co-founder, Doppel
Works with
Pushes campaign-level context into existing SIEM and SOAR through the Agentic Security Surface. Public references (opens in new tab) include OpenAI, Coinbase, and United Airlines, with additional customer detail in Doppel's published customer stories.
What you have now
CrowdStrike, SentinelOne, or Defender watching for known-bad behavior on the endpoint and missing most of what's actually installed there. Research cited by Glow (opens in new tab) puts the average enterprise endpoint running 12,000+ pieces of software, two-thirds of it invisible to current tooling.
What replaces it
Glow continuously discovers every piece of software on the endpoint, including the AI agents and browser extensions nobody approved, and remediates automatically before it becomes an incident, instead of waiting to detect and respond after something runs.
Founder on why the old way broke
"Prevention was always the right answer in security."
— Roi Tiger (opens in new tab), CEO and co-founder, Glow
Works with
Runs alongside existing EDR rather than ripping it out. Glow owns discovery, validation, and remediation of the software EDR was never built to see. EDR keeps its detection role for known threats.
What you have now
A CSPM tool that scans your cloud configuration on a schedule and hands you a list of theoretical misconfigurations, most of them already stale by the time someone reads the report.
What replaces it
Upwind reads runtime signals continuously and tells you what's actually reachable and exploitable right now, correlating posture, vulnerability, and identity findings against live evidence instead of a static snapshot.
Founder on why the old way broke
"Cloud security teams are tasked to do the impossible."
— Amiram Shachar (opens in new tab), CEO and co-founder, Upwind
Works with
Runtime evidence feeds prioritization into whatever ticketing or SOAR layer you already run, so a vulnerability finding is ranked by what's actually running, not just what's theoretically present.
What you have now
Legacy DLP: static rules and alert floods. Cyera's own launch materials cite eliminating over 95% of false-positive DLP alerts (opens in new tab) as the problem it was built to solve, which says less about Cyera and more about how much noise the category tolerated before it.
What replaces it
Cyera finds and classifies sensitive data everywhere it lives, including at rest, in motion, and in AI prompts, and remediates exposure automatically instead of generating another alert for a human to triage.
Founder on why the old way broke
"Everyone loves to hate DLP, and for good reason."
— Yotam Segev (opens in new tab), CEO and co-founder, Cyera
Works with
Publicly demonstrated pairing detected data exposures with SOC automation platforms — Cyera and Torq (opens in new tab) have jointly shown this integration, so a data-risk finding triggers remediation instead of sitting in a queue.
What you have now
A scanner that finds the CVE, opens a ticket, and waits on a human to chase down the system owner, apply a fix, and confirm it actually worked. Cogent's own research (opens in new tab) found the average time from disclosure to a working exploit collapsed from 125.3 days to 0.5 days between January 2025 and April 2026, while the average enterprise still takes about 60 days to close a critical vulnerability.
What replaces it
Cogent's agents identify exposure, determine ownership, apply the fix, and verify it's resolved end to end, without a human sitting in the ticket queue for each step. The company is young, founded in 2024 (opens in new tab), so this is a newer track record than the incumbents it's replacing.
Founder on why the old way broke
"The math on vulnerability management has changed."
— Vineet Edupuganti (opens in new tab), CEO and co-founder, Cogent
Works with
Connects to existing scanners and vulnerability data sources, normalizing what they find and taking over the coordination and verification work a human used to do manually.
What you have now
A CVE scanner that tells you a package was vulnerable after a researcher already found and reported it. That's not the same as catching a malicious package the moment someone tries to install it.
What replaces it
Socket inspects what a package actually does, including the behavior, not just the version number, and blocks malicious ones at install time, before a CVE exists for anyone to look up.
Founder on why the old way broke
"They know these products are deeply embedded, highly trusted, and often massively overprivileged."
— Feross Aboukhadijeh (opens in new tab), founder and CEO, Socket
Works with
Plugs into CI/CD pipelines, npm, PyPI, and GitHub directly, so blocking happens at the point a dependency enters the build, not in a report someone reads later.
What you have now
A SOAR platform running static playbooks that break the moment a vendor updates an API, maintained by the two engineers on the team who actually understand the scripts.
What replaces it
Torq’s agents automate alert investigation and case handling, with human review for cases requiring escalation, including the ones no playbook was ever written for, and close the case instead of routing it to a human queue. Torq's own materials put legacy SOAR's alert coverage at roughly 30–40% of volume (opens in new tab), with the rest never investigated.
Founder on why the old way broke
"We are witnessing the end of the legacy SOC."
— Ofer Smadari (opens in new tab), CEO and co-founder, Torq
Works with
Built as connective tissue by design. No-code, API-first integration (opens in new tab) across SIEM, EDR, DSPM, IAM, and XDR tools, so a detection from any layer can trigger an automated response in Torq.
What you have now
Nothing, really. No live inventory exists today of the AI agents, MCP servers, and browser extensions that your own approved software is quietly running on its own.
What replaces it
This is a new layer, not a replacement. Neo inventories every piece of agentic software running inside the enterprise, flags excessive permissions, and holds risky agent actions, e.g., credential access, data exports, code changes, for policy-based approval before they execute. The company only came out of stealth on July 20, 2026 (opens in new tab), so this is the newest and least proven layer on this list.
Founder on why the old way broke
"Enterprise security was built for a world where software behaved predictably."
— Nicholas Warner (opens in new tab), CEO and co-founder, Neo
Works with
Sits at the control layer rather than the identity layer, governing what an agent is allowed to do, not authenticating who it is. So it's designed to complement identity and access tools rather than replace them.
These eight companies don't share a buyer, a category, or an investor. What they share is the same thinking: the old tool in their category couldn't be patched into working, so they replaced it outright rather than adding a chatbot on top.
That's what turns eight point solutions into one stack. A resolved case in one layer is a signal the next layer can use. For example: a Cyera-flagged data exposure routed through SOC automation, a Doppel-confirmed phishing campaign feeding a training drill, a Socket block informing what Cogent prioritizes next. Bidirectional SIEM and SOAR integration is what makes that flow possible instead of leaving eight tools sitting in eight separate tabs.
What's the difference between AI-native security and AI added to legacy security?
AI-native platforms are built so agents investigate and act inside the case lifecycle on their own, including reasoning about threats they haven't seen before. AI-augmented tools use AI to speed up triage and summarization while a human still drives the case.
The tell: what does it do without a human in the loop? A platform that executes at machine speed and only escalates the hard cases is AI-native. One that writes you a better summary of the same alert is AI-augmented.
Can one AI cybersecurity company cover endpoint, email, and social engineering, or do I need several?
Most enterprises need several layers. Endpoint vendors may go deep on devices but lack external impersonation capabilities. Email-focused vendors may offer limited coverage across the domain, social, and telco surface. Many enterprises pair an AI-native endpoint or cloud layer with a dedicated social engineering defense platform, then connect both through bidirectional SIEM and SOAR integrations.
How do I tell whether a cybersecurity company's AI is real or marketing?
Run a proof of concept against your actual environment. Ask exactly what the AI does without a human in the loop, whether it executes takedowns or remediation autonomously, or just stops at recommending an action. Then test whether it reasons about novel threats rather than only matching known signatures, and whether detection improves from the cases it resolves.
