VentureBeat just lately sat down (just about) with Itamar Golan, co-founder and CEO of Immediate Safety, to speak via the GenAI safety challenges organizations of all sizes face.
We talked about shadow AI sprawl, the strategic choices that led Golan to pursue constructing a market-leading platform versus competing on options, and a real-world incident that crystallized why defending AI functions isn't non-obligatory anymore. Golan supplied an unvarnished view of the corporate's mission to empower enterprises to undertake AI securely, and the way that imaginative and prescient led to SentinelOne's estimated $250 million acquisition in August 2025.
Golan's path to founding Immediate Safety started with educational work on transformer architectures, nicely earlier than they turned foundational to immediately's massive language fashions. His expertise constructing one of many earliest GenAI-powered safety features utilizing GPT-2 and GPT-3 satisfied him that LLM-driven functions had been creating a wholly new assault floor. He based Immediate Safety in August 2023, raised $23 million throughout two rounds, constructed a 50-person staff, and achieved a profitable exit in underneath two years.
The timing of our dialog couldn’t be higher. VentureBeat evaluation reveals shadow AI now prices enterprises $4.63 million per breach, 16% above common, but 97% of breached organizations lack primary AI entry controls, based on IBM's 2025 information. VentureBeat estimates that shadow AI apps might double by mid-2026 based mostly on present 5% month-to-month progress charges. Cyberhaven information reveals 73.8% of ChatGPT office accounts are unauthorized, and enterprise AI utilization has grown 61x in simply 24 months. As Golan instructed VentureBeat in earlier protection, "We see 50 new AI apps a day, and we've already cataloged over 12,000. Round 40% of those default to coaching on any information you feed them, which means your mental property can turn out to be a part of their fashions."
The next has been edited for readability and size.
VentureBeat: What made you acknowledge that GenAI safety wanted a devoted firm when most enterprises had been nonetheless determining learn how to deploy their first LLMs? Was there a selected second, buyer dialog, or assault sample you noticed that satisfied you this was a fundable, venture-scale alternative?
Itamar Golan: From an early age, I used to be drawn to arithmetic, information, and the rising world of synthetic intelligence. That curiosity formed my educational path, culminating in a research on transformer architectures, nicely earlier than they turned foundational to immediately's massive language fashions. My ardour for AI additionally guided my early profession as a knowledge scientist, the place my work more and more intersected with cybersecurity.
All the things accelerated with the discharge of the primary OpenAI API. Round that point, as a part of my earlier job, I teamed up with Lior Drihem, who would later turn out to be my co-founder and Immediate Safety's CTO. Collectively, we constructed one of many earliest safety features powered by generative AI, utilizing GPT-2 and GPT-3 to generate contextual, actionable remediation steps for safety alerts. This diminished the time safety groups wanted to grasp and resolve points.
That have made it clear that functions powered by GPT-like fashions had been opening a wholly new and susceptible assault floor. Recognizing this shift, we based Immediate Safety in August 2023 to deal with these rising dangers. Our aim was to empower organizations to journey this wave of innovation and unleash the potential of AI with out it changing into a safety and governance nightmare.
Immediate Safety turned identified for immediate injection protection, however you had been fixing a broader set of GenAI safety challenges. Stroll me via the total scope of what the platform addressed: information leakage, mannequin governance, compliance, crimson teaming, no matter else. What capabilities ended up resonating most with clients which will have shocked you?
From the start, we designed Immediate Safety to cowl a broad vary of use instances. Focusing solely on worker monitoring or prompt-injection safety for inner AI functions was by no means sufficient. To really give safety groups the arrogance to undertake AI safely, we would have liked to guard each touchpoint throughout the group, and do all of it at runtime.
For a lot of clients, the actual turning level was discovering simply what number of AI instruments their staff had been already utilizing. Early on, corporations usually discovered not simply ChatGPT however dozens of unmanaged AI companies in lively use utterly exterior IT's visibility. That made shadow AI discovery a vital a part of our resolution.
Equally essential was real-time sensitive-data sanitization. As a substitute of blocking AI instruments outright, we enabled staff to make use of them safely by routinely eradicating delicate data from prompts earlier than it ever reached an exterior mannequin. It struck the steadiness organizations wanted: robust safety with out sacrificing productiveness. Workers might preserve working with AI, whereas safety groups knew that no delicate information was leaking out.
What shocked many purchasers was how enabling protected utilization — somewhat than proscribing it — drove quicker adoption and belief. As soon as they noticed AI as a managed, safe channel as an alternative of a forbidden one, utilization exploded responsibly.
You constructed Immediate Safety right into a market chief. What had been the 2 to a few strategic choices that truly accelerated your progress? Was it specializing in a selected vertical?
Wanting again, the actual acceleration didn't come from luck or timing: It got here from a couple of deliberate selections I made early. These selections had been uncomfortable, costly, and slowed us down within the brief time period, however they created large leverage over time.
First, I selected to construct a class, not a function. From day one, I refused to place Immediate Safety as "simply" safety towards immediate injection or information leakage, as a result of I noticed that as a useless finish.
As a substitute, I framed Immediate because the AI safety management layer for the enterprise, the platform that governs how people, brokers, and functions work together with LLMs. That call was elementary, permitting us to create a price range as an alternative of combating for it, sit on the CISO desk as a strategic layer somewhat than a device, and construct platform-level pricing and long-term relevance as an alternative of a slender level resolution. I wasn't attempting to win a function race; I used to be constructing a brand new class.
Second, I selected enterprise complexity earlier than it was snug. Whereas most startups keep away from complexity till they're compelled into it, I did the other: I constructed for enterprise deployment fashions early, together with self-hosted and hybrid; lined actual enterprise surfaces like browsers, IDEs, inner instruments, MCPs, and agentic workflows; and accepted longer cycles and extra advanced engineering in alternate for credibility. It wasn't the best route, but it surely gave us one thing rivals couldn't pretend: enterprise readiness earlier than the market even knew it will want it.
Third, I selected depth over logos. Moderately than chasing quantity or vainness metrics, I went deep with a smaller variety of very critical clients, embedding ourselves into how they rolled out AI internally, how they considered threat, coverage, and governance, and the way they deliberate long-term AI adoption. These clients didn't simply purchase the product: they formed it. That created a product that mirrored enterprise actuality, produced proof factors that moved boardrooms and never simply safety groups, and constructed a stage of defensibility that got here from entrenchment somewhat than advertising and marketing.
You had been educating the market on threats most CISOs hadn't even thought of but. How did your positioning and messaging evolve from yr one to the acquisition?
Within the early days, we had been educating a market that was nonetheless attempting to grasp whether or not AI adoption prolonged past a couple of staff utilizing ChatGPT for productiveness. Our positioning centered closely on consciousness, displaying CISOs that AI utilization was already sprawling throughout their organizations and that this created actual, instant dangers they hadn't accounted for.
I wasn't attempting to win a function race; I used to be constructing a brand new class.
Because the market matured, our messaging shifted from "that is taking place" to "right here's the way you keep forward." CISOs now absolutely acknowledge the dimensions of AI sprawl and know that easy URL filtering or primary controls received't suffice. As a substitute of debating the issue, they're on the lookout for a method to allow protected AI use with out the operational burden of monitoring each new device, web site, copilot, or AI agent staff uncover.
By the point of the acquisition, our positioning centered on being the protected enabler: an answer that delivers visibility, safety, and governance on the velocity of AI innovation.
Our analysis reveals that enterprises are struggling to get approvals from senior administration to deploy GenAI safety instruments. How are safety departments persuading their C-level executives to maneuver ahead?
Probably the most profitable CISOs are framing GenAI safety as a pure extension of present information safety mandates, not an experimental price range line. They place it as defending the identical belongings, company information, IP, and person belief, in a brand new, quickly rising channel.
What's essentially the most critical GenAI safety incident or near-miss you encountered whereas constructing Immediate Safety that basically drove residence how vital these protections are? How did that incident form your product roadmap or go-to-market method?
The second that crystallized every little thing for me occurred with a big, extremely regulated firm that launched a customer-facing GenAI assist agent. This wasn't a sloppy experiment. That they had every little thing the safety textbooks suggest: WAF, CSPM, shift-left, common crimson teaming, a safe SDLC, the works. On paper, they had been doing every little thing proper.
What they didn't absolutely account for was that the AI agent itself had turn out to be a brand new, uncovered assault floor. Inside weeks of launch, a non-technical person found that by fastidiously crafting the fitting dialog circulate (not code, not exploits, simply pure language) they may prompt-inject the agent into revealing data from different clients' assist tickets and inner case summaries. It wasn't a nation-state attacker. It wasn't somebody with superior expertise. It was basically a curious person with time and creativity. And but, via that single conversational interface, they managed to entry a few of the most delicate buyer information the corporate holds.
It was each fascinating and terrifying: realizing how creativity alone might turn out to be an exploit vector.
That was the second I actually understood what GenAI modifications concerning the risk mannequin. AI doesn't simply introduce new dangers, it democratizes them. It makes programs hackable by individuals who by no means had the talent set earlier than, compresses the time it takes to find exploits, and massively expands the harm radius as soon as one thing breaks. That incident validated our authentic method, and it pushed us to double down on defending AI functions, not simply inner use. We accelerated work round:
• Runtime safety for customer-facing AI apps
• Immediate injection and context manipulation detection
• Cross-tenant information leakage prevention on the mannequin interplay layer
It additionally reshaped our go-to-market. As a substitute of solely speaking about inner AI governance, we started displaying safety leaders how GenAI turns their customer-facing surfaces into high-risk, high-exposure belongings in a single day.
What's your position and focus now that you simply're a part of SentinelOne? How has working inside a bigger platform firm modified what you're capable of construct in comparison with working an impartial startup? What obtained simpler, and what obtained tougher?
The main focus now could be on extending AI safety throughout your entire platform, bringing runtime GenAI safety, visibility, and coverage enforcement into the identical ecosystem that already secures endpoints, identities, and cloud workloads. The mission hasn't modified; the attain has.
Finally, we're constructing towards a future the place AI itself turns into a part of the protection material: not simply one thing to safe, however one thing that secures you.
The larger image
M&A exercise continues to speed up for GenAI startups which have confirmed they will scale to enterprise-level safety with out sacrificing accuracy or velocity. Palo Alto Networks paid $700 million for Shield AI. Tenable acquired Apex for $100 million. Cisco purchased Sturdy Intelligence for a reported $500 million. As Golan famous, the businesses that survive the following wave of AI-enabled assaults might be people who embedded safety into their AI adoption technique from the start.
Publish-acquisition, Immediate Safety's capabilities will lengthen throughout SentinelOne's Singularity Platform, together with MCP gateway safety between AI functions and greater than 13,000 identified MCP servers. Immediate Safety can also be delivering model-agnostic protection throughout all main LLM suppliers, together with OpenAI, Anthropic, and Google, in addition to self-hosted or on-prem fashions as a part of the corporate's integration into the Singularity Platform.
