My name is Aleix Pérez, and I'm an early-stage tech VC investor based in Barcelona, with a deep focus on AI, cybersecurity, software and data infrastructure. You can reach out to me at aleixperez@me.com for any inquiries.

💰 Cybersecurity Funding rounds in July 2026

💰 Cybersecurity Acquisitions in July 2026

Data Methodology and Sources

• Secondaries are excluded.

• Service and consulting-focused companies are excluded.

• Financial figures are standardized and reported in U.S. dollars (USD) at the time of collection.

• Organizations are classified in segments according to the Cyberflow framework.

• Details such as lead investors, deal amounts, or deal stages may be revised after initial release.

• If you notice any inaccuracies, please let us know so we can correct them.

Disclaimer

This post and the information presented are intended for informational purposes only. While certain information contained herein has been obtained from sources believed to be reliable, neither the author nor any of his employers or their affiliates have independently verified this information, and its accuracy and completeness cannot be guaranteed. Accordingly, no representation or warranty, express or implied, is made as to, and no reliance should be placed on, the fairness, accuracy, timeliness or completeness of this information. The author and affiliated persons and companies assume no liability for this information and no obligation to update the information or analysis contained herein in the future.

The reason is structural: the perimeter is gone. Companies today run on hundreds of cloud services, thousands of human accounts, and a fast-growing population of non-human identities: service accounts, API keys, OAuth tokens, and now autonomous AI agents. In a typical enterprise, machine identities already outnumber humans by 50 to 1, and agentic AI is accelerating that ratio every quarter. Every agent an enterprise deploys is a new identity with credentials, permissions, and access to critical systems. Agents spawn sub-agents, chain API calls, and act at machine speed. Most organizations can’t even inventory them, let alone govern them.

Attackers noticed first. Roughly three out of four breaches today involve compromised credentials or privilege abuse, and the largest incidents of recent years, fromMGM to Change Healthcare to the Snowflake customer compromises, all share thesame root: identity. Attackers no longer break in. They log in. Andincreasingly, what logs in isn’t human. Once inside, the earliest stages of an identity kill chain (reconnaissance, lateral movement, privilege escalation) happen exactly where endpoint and log-centric tools go blind.

The industry’s answer has been more fragmentation: IAM here, PAM there, an ISPM point solution, an ITDR add-on, and a compliance team working from spreadsheets. But this is not a vendor problem; it is an architecture problem. Today’s identity and detection stacks were architected for a world of employees logging into web apps. You cannot bolt an agentic future onto them. You have to build for it from the start. Meanwhile, European regulation (NIS2, DORA, ENS) is turning identity governance from a best practice into a legal obligation, audited continuously, and regulators are starting to ask who governs the agents.

That combination of a new control plane under attack, an agentic identity explosion, legacy architectures, and regulatory pressure is exactly the kind of market shift we look for.

It’s why we invested in 8Layers.

‍

Our conviction starts with the team.

The four founders, Daniel García, Pedro Palao, Iago Salgado, and Pablo Carretero, are core alumni of Devo, the Spanish cybersecurity and data platform that scaled to nine-figure revenue and a $2B valuation. Between them they led engineering, data infrastructure, systems, and threat research at Devo, and most of the current team followed them from there.

This matters for a specific reason: securing an agentic enterprise is, at its core, a data and AI problem. Every sophisticated detection requires correlating massive volumes of signals from wildly heterogeneous sources (IdPs, cloud workloads, logs, external enrichment) over long time windows, and feeding that context into models that can separate legitimate agent behavior from an attack in progress. This team has spent more than a decade building exactly that kind of infrastructure at enterprise scale. Few founding teams in Europe, or anywhere, can claim the same.

‍

The product reflects that DNA.

8Layers takes a different approach: unifying ISPM, ITDR and continuous compliance on a single shared identity data layer, covering human and non-human identities alike, from service accounts to AI agents.

That integration is not cosmetic. The same identity inventory that powers detection generates audit evidence; a risk accepted in posture management is automatically reflected in compliance reporting; a compliance drift triggers a security alert. Detections are context-aware, enriched by ML models operating over the full identity graph, and response is automated: rules can suspend an identity, revoke sessions, or isolate a compromised agent without an analyst in the loop. That last part matters, because attacks that move at machine speed cannot wait for human triage.

And because the root problem is architectural, the team is rebuilding the foundation itself: a proprietary, graph-based, self-shaping database that observes usage patterns and adapts its own structure, designed for the continuous, long-context correlation that identity detection and AI models demand and that conventional architectures handle poorly. It is an ambitious, multi-year bet, and it is the long-term moat.

There’s also a European angle we think is under appreciated. Compliance with ENS, NIS2,and ISO 27001 is native to the platform, not retrofitted from a generic playbook. As European regulation becomes a direct demand catalyst, and as AI governance frameworks extend those obligations to agentic systems, being built for it from day one is a real go-to-market advantage.

‍

Every great cybersecurity company sits at the intersection of three things: an urgent problem, a structural market shift, and a team uniquely built to solve it.

The problem is urgent: identity is the number one attack vector, and the agentic AI wave is multiplying the surface faster than organizations can govern it.

The shift is real: ISPM, ITDR and non-human identity security are emerging categories with no established winner, and both AI adoption and European regulation are pulling demand forward.

And the team is exceptional: operators who have already built and scaled category-defining security data infrastructure, now rebuilding the identity layer for the agentic enterprise.

That’s why we’re proud to back Daniel, Pedro, Iago, Pablo, and the 8Layers team as they build the identity security platform for the AI era.

Workforce Identity Security landscape and where 8layers sits.

The identity security market is fragmented across dozens of subsegments, fromaccess management and IGA to PAM, NHI, and detection. We mapped it ourselves to see where the real gaps are and where 8layers fits.

8Layers sits at the intersection of ITDR and ISPM, with an agentic-native architecture from day one. That is the corner of the map where legacy vendors are still bolting AI onto old platforms, and where cloud-first challengers are still stuck on posture without real-time response. It is nearly empty, and it is where the market is heading.

‍Learn more at www.8layers.io

My name is Aleix Pérez, and I'm an early-stage tech VC investor based in Barcelona, with a deep focus on AI, cybersecurity, software and data infrastructure. You can reach out to me at aleixperez@me.com for any inquiries.

💰 Cybersecurity Funding rounds in June 2026

If you'd like full access to the Airtable with the complete list of cybersecurity deals from 2025 and 2026, feel free to reach out at aleixperez@me.com.

Data Methodology and Sources

• Grants and secondaries are excluded.

• Service and consulting-focused companies are excluded.

• Financial figures are standardized and reported in U.S. dollars (USD) at the time of collection.

• Organizations are classified in segments according to the Cyberflow framework.

• Details such as lead investors, deal amounts, or deal stages may be revised after initial release.

• If you notice any inaccuracies, please let us know so we can correct them.

Disclaimer

This post and the information presented are intended for informational purposes only. While certain information contained herein has been obtained from sources believed to be reliable, neither the author nor any of his employers or their affiliates have independently verified this information, and its accuracy and completeness cannot be guaranteed. Accordingly, no representation or warranty, express or implied, is made as to, and no reliance should be placed on, the fairness, accuracy, timeliness or completeness of this information. The author and affiliated persons and companies assume no liability for this information and no obligation to update the information or analysis contained herein in the future.

My name is Aleix Pérez, and I'm an early-stage tech VC investor based in Barcelona, with a deep focus on AI, cybersecurity, software and data infrastructure. You can reach out to me at aleixperez@me.com for any inquiries.

💰 Cybersecurity Funding rounds in May 2026

If you'd like full access to the Airtable with the complete list of cybersecurity deals from 2025 and 2026, feel free to reach out at aleixperez@me.com.

Data Methodology and Sources

• Grants and secondaries are excluded.

• Service and consulting-focused companies are excluded.

• Financial figures are standardized and reported in U.S. dollars (USD) at the time of collection.

• Organizations are classified in segments according to the Cyberflow framework.

• Details such as lead investors, deal amounts, or deal stages may be revised after initial release.

• If you notice any inaccuracies, please let us know so we can correct them.

Disclaimer

This post and the information presented are intended for informational purposes only. While certain information contained herein has been obtained from sources believed to be reliable, neither the author nor any of his employers or their affiliates have independently verified this information, and its accuracy and completeness cannot be guaranteed. Accordingly, no representation or warranty, express or implied, is made as to, and no reliance should be placed on, the fairness, accuracy, timeliness or completeness of this information. The author and affiliated persons and companies assume no liability for this information and no obligation to update the information or analysis contained herein in the future.

My name is Aleix Pérez, and I'm an early-stage tech VC investor based in Barcelona, with a deep focus on AI, cybersecurity, software and data infrastructure. You can reach out to me at aleixperez@me.com for any inquiries.

💰 Funding rounds in March 2026:

Part I (deals 1-22):

Part II (deals 23-30):

If you'd like full access to the Airtable with the complete list of cybersecurity deals from 2025 and 2026, feel free to reach out at aleixperez@me.com.

Data Methodology and Sources

• Grants and secondaries are excluded.

• Service and consulting-focused companies are excluded.

• Financial figures are standardized and reported in U.S. dollars (USD) at the time of collection.

• Organizations are classified in segments according to the Cyberflow framework.

• Details such as lead investors, deal amounts, or deal stages may be revised after initial release.

• If you notice any inaccuracies, please let us know so we can correct them.

Disclaimer

This post and the information presented are intended for informational purposes only. While certain information contained herein has been obtained from sources believed to be reliable, neither the author nor any of his employers or their affiliates have independently verified this information, and its accuracy and completeness cannot be guaranteed. Accordingly, no representation or warranty, express or implied, is made as to, and no reliance should be placed on, the fairness, accuracy, timeliness or completeness of this information. The author and affiliated persons and companies assume no liability for this information and no obligation to update the information or analysis contained herein in the future.

That model is now breaking. When AI-powered attacks move from compromise to exfiltration in under 30 minutes, and when Anthropic’s Mythos can autonomously chain four vulnerabilities into a browser sandbox escape overnight, annual pentesting is no longer a security strategy. It’s a compliance ritual.

A new category is emerging — Autonomous Offensive Security — and it’s attracting some of the largest early-stage rounds in cybersecurity history. To make sense of the rapidly expanding vendor ecosystem, we’ve created an Offensive Security Market Map, categorizing companies across three waves: Incumbents, Challengers, and the new generation of AI-native autonomous players.

📩 Building in the space? Reach out — Aleix Perez (aperez@caixacapitalrisc.es

)

1. Why now? The offensive security inflection point ❓

Five forces are converging to make autonomous offensive security not just attractive, but necessary:

The speed gap is now untenable. Attackers powered by AI move at machine speed. CrowdStrike’s 2026 Global Threat Report documented an 89% increase in AI-enabled attacks, with average breakout times dropping to 29 minutes. Annual pentests that take weeks to schedule and execute can’t defend against adversaries that operate 24/7 at the speed of API calls.

Code volume is exploding. With more than 30% of new code now generated by AI, and enterprises deploying updates multiple times per day, the gap between what gets built and what gets tested is widening by the hour. Vibe coding has made software creation accessible to people who were never trained to think about security. The attack surface grows faster than any human team can audit.

The talent shortage is structural. The penetration testing market is estimated at $2.4–2.7 billion in 2025, growing at 12–15% CAGR. But most of this market still depends on a small pool of elite human testers. With 4.8 million unfilled cybersecurity positions globally, and offensive security expertise being the scarcest skillset of all, scaling through hiring is impossible.

Frontier models changed the game. Anthropic’s Mythos Preview found thousands of critical zero-days across every major operating system and browser — capabilities that emerged from general reasoning improvements, not specialized offensive training. If a general-purpose model can do this, purpose-built offensive AI agents will go further, faster.

The asymmetry structurally favors offense. Research from Anthropic and Google DeepMind shows that offensive operations produce immediate, verifiable feedback (breached or not), making them ideal for reinforcement learning. Defense has no equivalent signal. AI-generated phishing went from underperforming human red teams by 18% to outperforming them by 24% in just two years. The feedback loop is accelerating.

Regulation demands continuous validation. PCI DSS 4.0, NIS2, DORA, and HIPAA now require structured, auditable security testing. Compliance-driven demand is pushing organizations from ad-hoc annual tests to continuous offensive security programs.

The result: pentesting is transitioning from an episodic consulting engagement to an always-on, AI-powered security function. The companies in this map are building that future.

2. The Three Waves 🌊

Wave 1 — Incumbents (Pre-2015)

Rapid7, Astra, Synack, Cobalt, Pentera, HackerOne, YesWeHack, Intigriti, Bugcrowd, BishopFox

The foundation of modern offensive security: managed pentesting platforms, bug bounty marketplaces, and BAS tools. Strong brand, deep distribution, large researcher networks, but fundamentally human-dependent. Bug bounty platforms rely on crowdsourced researchers. Managed pentest firms sell consultant hours. Even BAS players like Pentera run predefined scenarios rather than reasoning through novel attack paths.

Several are evolving, Pentera has acquired AI capabilities, Cobalt moved toward PTaaS, HackerOne is integrating AI triage, but the underlying architecture was not built for autonomous, continuous operation.

Wave 2 — Challengers (2015–2023)

Horizon3.ai, Cymulate, Sprocket Security, Picus, Aptori, CyCognito, Strike, Theori, FireCompass, Oneleet, Patrowl, Intruder, PlexTrac

Pre-LLM innovators that pushed beyond manual pentesting into automation, continuous testing, and attack surface management. Horizon3.ai and Cymulate pioneered continuous validation. CyCognito and FireCompass built external attack surface discovery. Picus and Cymulate created BAS platforms mapped to MITRE ATT&CK. PlexTrac streamlined reporting and remediation workflows.

Many are now layering AI on top of existing platforms, but their core architectures were not designed for the agentic, reasoning-based approach that defines the next generation.

Wave 3 — Autonomous Offensive Security (2024–Now)

Armadin, Tenzai, Aikido, Terra Security, XBOW, Hadrian, Runsybil, Cracken, Novee, Equixly, Method, Aiko Corp, Escape, Depthfirst, Copilot, Hacktron, Ghost, Gecko Security, MindFort, Veria Labs, Penti, Staris, Shinobi Security, Stealthnet.ai, Specular, Ethiack, Hex Security, Zeropath, OffensAI, Assail

Plus an AI security sub-segment: Dreadnode, Harmony Intelligence, Mindgard, SPLX AI, Straiker, Virtue AI, Troj.AI, Calypso AI, HiddenLayer, Prompt Security, AIM Security, Enkrypt AI, Protect AI, Repello AI, Yrikka, NeuralTrust, Pillar, Adversa, Alice, Witness AI

The first true generation of AI-native offensive security platforms. Where Wave 2 automated known playbooks, Wave 3 reasons, adapts, and chains attacks like a skilled red teamer, at machine speed, continuously, across the entire attack surface.

What defines them:

Agentic attack swarms. Armadin, led by Mandiant founder Kevin Mandia, backed by $189.9M in the largest seed+Series A in cyber history, deploys specialized AI agents that continuously identify real kill chains. As Mandia put it: “You cannot have a human in the loop for every defense decision and expect to win.”

Validated exploitation, not scanning. XBOW outperformed thousands of human researchers on the HackerOne leaderboard. Their $120M Series C at $1B+ valuation validates the thesis.

Always-on operation. Tenzai, founded by the Guardicore team (acquired by Akamai for $600M), backed by a record $75M seed — builds an autonomous AI hacker that operates continuously. As CEO Pavel Gurvich noted: “Providing real security assurance is only possible with autonomous AI.”

Business context. Terra Security ingests documentation, API schemas, and cloud architecture to differentiate between a critical vulnerability on a sandbox and a medium one on a payment gateway. The “so what?” layer that makes findings actionable.

AI model security. A distinct sub-segment, Mindgard, HiddenLayer, Protect AI, Prompt Security and others, focuses on securing AI models themselves against prompt injection, model theft, and adversarial attacks. This is rapidly growing as enterprises deploy AI agents at scale.

3. The Market Opportunity 📈

The pentesting market is valued at $2.4–2.7 billion in 2025, projected to reach $5.5–7.4 billion by 2031–2034. But the real opportunity is much larger:

Pentesting is just the entry point. Autonomous platforms expand into vulnerability management, red teaming, attack surface management, and security validation — collectively a $15–20 billion opportunity.

Services are the real TAM. As Tenzai’s Gurvich noted: “For every dollar an organization spends on security products, it spends about five on services.” The addressable market is the security services market, not just pentesting software.

The coverage gap is the unlock. Enterprises currently test only 20–25% of applications due to cost constraints. AI platforms enabling 100% coverage unlock 4–5x the current market in latent demand.

The capital flowing in reflects these expectations. Armadin’s $189.9M is the largest early-stage raise in cybersecurity history. The investor thesis is clear: autonomous offensive security is a category-defining opportunity.

4. The hidden battleground: Training data 🛢

Where does the training data come from, and who owns it? This may be the single most important competitive dynamic in the category. The best offensive security data, real exploitation chains, attack decision logic, red team methodologies, sits behind NDAs and in the heads of a few thousand elite practitioners. Unlike NLP or computer vision, there’s no ImageNet for hacking.

Four strategies are emerging:

The open foundation. CVE databases, disclosed exploits, security research papers. Everyone has these — table stakes, zero differentiation.

The expert factory. Hiring elite pentesters to generate labeled training data. Armadin’s approach: “reinforcing decades of human-led red teaming expertise into AI models.” Exceptional quality, but six-figure salaries don’t scale linearly.

The synthetic lab. Multi-agent architectures where AI sub-agents attack and defend against each other via reinforcement learning. Scales beautifully — the question is whether synthetic environments produce patterns that transfer to real-world exploitation.

The crowd. Turning bug bounty communities into data generation engines. Strong network effects, but data is only as valuable as its relevance to your target attack surface.

Our view: narrow data advantages (public CVEs) get compressed as foundation models improve. Proprietary attack methodology captured at scale becomes the defining competitive moat.

5. What happens to pentest services firms? 🧑‍💻

Manual testing still accounts for ~75% of the market. Most pentesting revenue flows to consulting firms, boutique shops, and Big 4 practices through human labor — thousands of firms billing $150–300/hour for work that is skilled but inherently unscalable.

The disruption math is straightforward. XBOW delivers expert-grade reports starting at $4,000 for assessments that cost $10,000–35,000 from a human team. When output quality converges, and HackerOne leaderboard results suggest it already is, pricing pressure will be severe.

But the story bifurcates, much like the MSSP market under AI pressure:

The firms that adopt get dramatically more efficient. A boutique with 15 pentesters that integrates autonomous platforms could cover 5–10x more clients — using AI for breadth and continuous coverage, deploying humans for the creative work AI can’t replicate: adversarial red teaming, social engineering, physical security, novel attack research. Their value proposition shifts from “we send you people” to “we deliver continuous offensive assurance.”

The firms that don’t adopt get commoditized. Two-week manual engagements and PDF reports compete on price against platforms delivering better coverage, faster, for less. The “annual pentest as compliance checkbox” becomes a race to the bottom.

The winning pentest firms of 2030 will operate as AI-augmented offensive security practices, autonomous platforms as the base layer, human expertise for complex engagements, selling outcomes instead of hours.

6. What comes next 🔮

Within three to five years:

Manual pentesting becomes boutique — reserved for nation-state-grade red teaming, custom hardware, and classified environments. Everything else goes autonomous.

Continuous offensive security becomes standard — integrated into CI/CD, triggered at every deployment, running 24/7. The “annual pentest” becomes as antiquated as annual antivirus scans.

Offensive and defensive merge — the next great security company will combine vulnerability management, threat hunting, pentesting, and offensive security into a continuous loop.

Incumbents acquire or get displaced. Wave 1 players with distribution but legacy architectures will either buy Wave 3 technology or lose relevance.

The era of the AI red team has arrived. The companies on this map are building its foundation.

📩 Building in the space or want to discuss the market? Reach out — Aleix Perez (aperez@caixacapitalrisc.es)

The narrative was simple and seductive: AI will automate vulnerability detection, replace junior SOC analysts, find and exploit bugs faster than any human, and eat cybersecurity whole. After all, if a general-purpose model, one that wasn't even specifically trained for offensive security, can discover thousands of zero-days and write working exploits that outperform tooling stacks built over decades at a cost of hundreds of millions, then what exactly are cybersecurity vendors still selling?

The reality is more nuanced, and ultimately more bullish for the industry, even if it’s painful for parts of it. AI will likely expand total cybersecurity spending while simultaneously destroying value in specific categories and compressing margins in others. The market is right that something fundamental is changing, but It’s wrong about what.

Two Events, One Misguided Narrative 📰

Let’s disentangle what actually happened, because the market treated two very different Anthropic announcements as the same story.

The February code-scanning tool is narrow. It scans source code for vulnerabilities and suggests fixes for human review. It operates in the development phase, long before software becomes operational. Bank of America’s analysts were clear: this tool only poses a significant threat to code-scanning platforms like GitLab and JFrog. It does not have the visibility, control, or reliability to replace endpoint protection, network security, or identity management.

Mythos is a different animal entirely. It’s a general-purpose frontier model whose cybersecurity capabilities emerged not from specialized training but from general improvements in coding, reasoning, and autonomy. It has found thousands of zero-day vulnerabilities (many critical) and can autonomously write working exploits, reverse-engineer closed-source binaries, and execute multi-stage attacks on vulnerable networks. The UK’s AI Security Institute confirmed it completed a 32-step corporate network attack simulation that would take human professionals 20 hours. Jamie Dimon said JPMorgan is testing it. Anthropic considers it too powerful to release publicly, restricting it to ~40 organizations under Project Glasswing with $100 million in usage credits.

This feels like a watershed moment — not because of the model itself, but because of what it represents. Vulnerability discovery and exploit development are moving from human timescales to machine timescales. A vulnerability that survived 27 years and five million fuzzing attempts gets found in a single overnight session. When that window compresses from weeks to hours, many long-held assumptions about patching cycles, incident response playbooks, and risk tolerance simply no longer hold. As the Cloud Security Alliance warned, the window between discovery and weaponization “has collapsed to hours.” Alex Stamos, former Facebook CISO, estimated defenders have roughly six months before open-weight models catch up.

But here’s the critical point: Mythos proves the cybersecurity bull case, not the bear case. Anthropic itself wrote that these capabilities will soon proliferate beyond actors committed to deploying them safely, and don’t forget that Crowdstrike and Palo Alto Networks are invited contributors in the Anthropic’s Project Glasswing. The shift to machine-speed offense demands machine-speed defense. That means more spending on security, not less, but it also means the kind of security that matters is changing fast.

What AI actually disrupts — and what It can’t touch 📊

SentinelOne CEO Tomer Weingarten has been vocal for years that application security, roughly a $20 billion segment and about 5% of total cybersecurity spending, was “incredibly risky and ripe for AI disruption.” He deliberately chose not to acquire into it while competitors spent billions doing so. The February sell-off validated that call. Many legacy, rule-based code-scanning tools are being commoditized. AI models are simply better, faster, and cheaper at reading and reasoning through static code.

But even within application security, finding vulnerabilities was never the hard part. As Scale Venture Partners argued in a recent essay, the real challenge is deciding what to do about them. Anthropic’s own blog noted that over 99% of the vulnerabilities Mythos uncovered haven’t been patched. That’s not a tooling failure, it’s a decisioning problem. 91% of companies have knowingly released applications with unresolved vulnerabilities. 95% of open-source vulnerabilities come from transitive dependencies, meaning patching one thing can cascade unpredictably. Accepting certain vulnerabilities behind compensating controls is a formally recognized practice in NIST 800-53, PCI-DSS, and ISO 27001. No model has earned the right, or the liability exposure, to make those calls autonomously.

And this is where the logic gets truly counterintuitive: Mythos doesn’t shrink the need for security tooling, it explodes it. Mythos reportedly surfaced thousands of high-severity zero-days in weeks. As models like it become widely available, the volume of findings requiring a judgment call will grow by orders of magnitude, and teams won’t be able to run that process through a general-purpose model or a chat interface. If any percentage of those findings are ones an organization can’t or won’t fix, and historically that percentage is very high, then a portion of every organization’s attack surface should be assumed exploitable. The question then shifts from “can we find all the bugs?” to “how quickly can we detect and remediate bad actors exploiting the gaps we know exist?” That is not a world with less cybersecurity. That is a world that demands an entirely new class of proactive security, what Scale VP describes as a net-new category combining elements of vulnerability management, threat hunting, penetration testing, and offensive security, purpose-built for the AI era.

As Contrast Security CISO David Lindner told Fortune: Mythos does little to help solve social engineering, hackers impersonating your boss or your IT department to gain access. Finding bugs is one problem. Defending the entire organization is another.

Now zoom out. The segments where AI cannot substitute for purpose-built infrastructure are massive:

• Endpoint Detection & Response: $5.6 billion in 2025, projected to reach $32 billion by 2033 at 24.6% CAGR

• Network security: $25–85 billion in 2025 (depending on scope definition), projected to reach $48–120 billion by 2030–2031 at 7–13% CAGR

• Identity security: $22–26 billion in 2025 for IAM alone, projected to reach $43 billion by 2030 at 10.4% CAGR — increasingly the critical control plane for zero-trust architectures, and growing even faster when including identity verification and governance

• Cloud security: $41–51 billion in 2025, projected to reach $91 billion by 2030 at 12.2% CAGR — the fastest-growing established segment, driven by multi-cloud adoption and the $32B Wiz acquisition

• Data security: $15–34 billion in 2025, projected to reach $33–78 billion by 2030 at 17–18% CAGR — driven by ballooning data volumes, privacy mandates like GDPR and DORA, and the fact that 82% of breaches now involve cloud-hosted data

• AI security: an emerging but fast-growing category opening a new attack surface at $3–31 billion in 2025 (depending on scope), projected to reach $86–94 billion by 2030 at 22–24% CAGR — every enterprise deploying AI models, agents, and copilots now needs to secure them, creating an entirely new layer of demand that didn’t exist three years ago

Weingarten’s key distinction is sharp: “There is a massive difference between finding a bug in a static environment and stopping a live, adversarial cyberattack in real-time.” The vendors built on autonomous, AI-native architectures will leverage this moment. The platforms relying on legacy patchworks of acquired point solutions have a harder road.

The Arms Race: AI powers both sides 🤼

Every AI capability that helps defenders is simultaneously available to attackers. As Anthropic acknowledged: “The same improvements that make the model substantially more effective at patching vulnerabilities also make it substantially more effective at exploiting them.” Mythos wasn’t trained for offensive security, its exploit-writing capabilities emerged from general reasoning improvements. Every frontier lab pushing model capabilities is, whether they intend to or not, also building tools adversaries will eventually access.

The data is stark: CrowdStrike’s 2026 Global Threat Report documented an 89% increase in AI-enabled attacks. IBM’s 2026 X-Force Index reported a 44% increase in attacks exploiting public-facing applications. Average breakout time dropped to 29 minutes. AI-generated phishing now achieves a 54% click-through rate versus 12% for traditional campaigns. 87% of WEF respondents identified AI-related vulnerabilities as the fastest-growing cyber risk.

Meanwhile, every new AI deployment inside an enterprise — agentic workflows, vibe-coded apps, AI-powered bots — is a new thing that needs to be secured. Anthropic itself learned this the hard way when its Claude Code model was manipulated in September 2025 to carry out a cyber-espionage operation across 30 organizations — the first confirmed case of an AI agent handling most steps of an intrusion normally performed by human hackers. As Weingarten put it: AI itself is now “the most prominent new attack surface.” Or as CrowdStrike CEO George Kurtz framed it after the Mythos announcement: “AI is creating the largest security demand driver since enterprises moved to the cloud.”

Jobs will change. The market won’t shrink🧑‍💻

Will AI automate L1 SOC tasks? Yes. Alert triage, log correlation, ticket drafting — these are already being automated. Job postings for security analysts have fallen roughly 53% since 2022. But the cybersecurity industry has 4.8 million unfilled positions globally. This is not a sector with a labor surplus being disrupted, it’s one with a chronic talent shortage where AI is finally helping close the gap.

L2 roles — deeper investigation, incident analysis, threat hunting — aren’t being eliminated; they’re being compressed and elevated. Many organizations are merging Tier-1 and Tier-2 as AI handles initial triage, flattening the traditional SOC. The threats reaching humans are the ones AI couldn’t resolve autonomously, meaning L2 work becomes harder and more critical. The human role shifts from processing to judgment.

But here’s the nuance the post would be dishonest to ignore: a huge share of cybersecurity spending is labor, not software. MSSPs, MDR providers, penetration testing firms, GRC consultants, incident response retainers — many of these are effectively labor-arbitrage businesses. If AI reduces the marginal cost of delivering security services by 50–80%, customers will eventually demand lower prices. An MSSP with 1,000 analysts today may need 300 in five years. A pentest firm may deliver the same output with a fraction of the staff. Revenue per customer could fall even as demand grows — creating intense pressure to adopt AI not as an option, but as an operational necessity to maintain and improve margins.

Models need infra, and infra has owners

Palo Alto Networks’ Nikesh Arora has made this point forcefully: frontier models are powerful, but they are not effective as comprehensive defense systems on their own. They never will be. They need scaffolding — sensors across the network, cloud, endpoints, and browsers; AI-enabled data lakes that convert noise into actionable intelligence; and consolidated security stacks that break the silos where critical signals get buried. “Models cannot fix what sensors cannot see.”

Research shows that in 75% of breaches, logging existed that should have flagged anomalous behavior, but critical signals were buried across fragmented tools. That gap was manageable when attacks moved at human speed. At AI speed, it becomes untenable. Consolidation is not a modernization preference. It is a prerequisite.

The cybersecurity industry spent decades building this scaffolding. The sensors, data lakes, telemetry pipelines, and detection models trained on billions of real-world events — none of this gets replaced by a frontier model. It gets amplified by it. The solution is not to pit LLMs against cybersecurity. It’s to integrate them into the defensive stack that already exists.

The market will grow. Not everyone will win 📈

The cybersecurity market was valued at $228–272 billion in 2025 depending on the source, with projections ranging from $352 billion by 2030 (MarketsandMarkets) to $663 billion by 2033 (Grand View Research). Cybersecurity Ventures forecasts $1 trillion in annual spending by 2031.

But market growth does not mean all incumbents win. The internet expanded total commerce, but many retailers were destroyed. Cloud computing expanded IT spending, but many on-prem vendors collapsed. AI could do the same: more spending overall, fewer vendors, greater platform consolidation, higher concentration of value in a few winners.

Google’s $32 billion acquisition of Wiz illustrates both sides. It’s a massive vote of confidence in cybersecurity’s future, and evidence that the market is consolidating around integrated platforms where control of the security layer is becoming disproportionately valuable. When attacks move at machine speed, CISOs cannot manage 70 fragmented products. They will consolidate vendors, cut overlapping tools, and demand measurable ROI. More spend overall, fewer vendors capturing value.

The real Winners and Losers 🏆

If the thesis is correct — AI expands total demand while reshaping where value accrues — then the question isn’t whether cybersecurity survives. It’s who wins and who gets disrupted.

Categories facing significant pressure:

• SAST / DAST / vulnerability scanning vendors — directly commoditized by frontier models

• Commoditized, low-value MSSPs selling basic alert monitoring — though AI-native MSSPs that adopt automation early may see their economics dramatically improve

• Standalone phishing and email security tools relying on content classification

• Point-solution vendors with weak proprietary data and no platform strategy

• Vendors whose core value proposition is human alert triage

Categories positioned to gain:

• Endpoint and network platforms with proprietary telemetry (CrowdStrike, Palo Alto, SentinelOne)

• Cloud security platforms with deep multi-cloud integration

• Identity security / NHI — increasingly the critical control plane

• AI-native SOC and autonomous response platforms

• Vendors with deep workflow integration, distribution, and consolidated stacks

• The emerging category of AI-era proactive security — combining vulnerability management, threat hunting, pen-testing, and offensive security into a continuous loop that fixes what should be fixed, surfaces exposure proactively, and builds detections for the gaps that can’t be closed

Conclusion

The panic over AI eating cybersecurity confuses a category-level disruption with an industry-level one. AI will commoditize vulnerability scanning. It will compress and transform labor-heavy services. It will accelerate platform consolidation. It will destroy the economics of point solutions that lack proprietary data or platform leverage.

But it will not shrink the cybersecurity market, because the same force that automates defense also automates offense, and in an arms race where attacks move at machine speed, the demand for protection only accelerates. The exposed attack surface is growing faster than ever. The winners won’t be general-purpose AI labs. They’ll be AI-native security platforms built with AI at their core, trained on real-world telemetry, deployed where it matters: on the endpoint, in the network, across the cloud, and consolidated enough to act at the speed the threat landscape now demands.

AI is unlikely to destroy cybersecurity. It is consolidating, evolving, and, in many ways, just getting started.

Disclaimer: This post reflects personal analysis and opinion. It does not constitute investment advice.

My name is Aleix Pérez, and I'm an early-stage tech VC investor based in Barcelona, with a deep focus on AI, cybersecurity, software and data infrastructure. You can reach out to me at aleixperez@me.com for any inquiries.

💰 Funding rounds in March 2026:

Part I (deals 1-23):

Part II (deals 24-45):

If you'd like full access to the Airtable with the complete list of cybersecurity deals from 2025 and 2026, feel free to reach out at aleixperez@me.com.

Data Methodology and Sources

• Grants and secondaries are excluded.

• Service and consulting-focused companies are excluded.

• Financial figures are standardized and reported in U.S. dollars (USD) at the time of collection.

• Organizations are classified in segments according to the Cyberflow framework.

• Details such as lead investors, deal amounts, or deal stages may be revised after initial release.

• If you notice any inaccuracies, please let us know so we can correct them.

Disclaimer

This post and the information presented are intended for informational purposes only. While certain information contained herein has been obtained from sources believed to be reliable, neither the author nor any of his employers or their affiliates have independently verified this information, and its accuracy and completeness cannot be guaranteed. Accordingly, no representation or warranty, express or implied, is made as to, and no reliance should be placed on, the fairness, accuracy, timeliness or completeness of this information. The author and affiliated persons and companies assume no liability for this information and no obligation to update the information or analysis contained herein in the future.

My name is Aleix Pérez, and I'm an early-stage tech VC investor based in Barcelona, with a deep focus on AI, cybersecurity, software and data infrastructure. You can reach out to me at aleixperez@me.com for any inquiries.

💰 Funding rounds in February 2026:

Part I (deals 1-23):

Part II (deals 24-47):

🤝 Exits in February 2026:

Data Methodology and Sources

• Grants and secondaries are excluded.

• Service and consulting-focused companies are excluded.

• Financial figures are standardized and reported in U.S. dollars (USD) at the time of collection.

• Organizations are classified in segments according to the Cyberflow framework.

• Details such as lead investors, deal amounts, or deal stages may be revised after initial release.

• If you notice any inaccuracies, please let us know so we can correct them.

Disclaimer

This post and the information presented are intended for informational purposes only. While certain information contained herein has been obtained from sources believed to be reliable, neither the author nor any of his employers or their affiliates have independently verified this information, and its accuracy and completeness cannot be guaranteed. Accordingly, no representation or warranty, express or implied, is made as to, and no reliance should be placed on, the fairness, accuracy, timeliness or completeness of this information. The author and affiliated persons and companies assume no liability for this information and no obligation to update the information or analysis contained herein in the future.

To address this limitation, I built an interactive visualization layer for the Cyberflow Year in Review 2025, allowing the dataset to be explored dynamically rather than interpreted through predefined charts or just rows.

The platform enables users to analyze the market across multiple dimensions, including:

• Funding activity by region and country

• Stage distribution and capital concentration

• Sector segmentation and emerging categories

• Investor participation and deal flow patterns

• Operational benchmarks, such as average FTE per round

• Temporal evolution of funding across 2025

The objective is straightforward: transform fragmented deal tracking into a structured intelligence resource that investors, founders, and operators can interrogate directly.

You can access the interactive dashboard here: https://lookerstudio.google.com/reporting/f2e9fece-3ded-42dd-9887-43167e128b2e/page/TTToF?s=hWsVpqjq9rs

Next Steps

This Year in Review represents an initial step toward building a continuously updated intelligence layer for the cybersecurity ecosystem. Future iterations will incorporate additional datasets, longitudinal comparisons, and deeper segmentation across emerging AI-native security categories.

As always, feedback from the community is welcome.

My name is Aleix Pérez, and I'm an early-stage tech VC investor based in Barcelona, with a deep focus on AI, cybersecurity, software and data infrastructure. You can reach out to me at aleixperez@me.com for any inquiries.

💰 Funding rounds in January 2026:

🤝 Exits in January 2026:

Data Methodology and Sources

• Grants and secondaries are excluded.

• Service and consulting-focused companies are excluded.

• Financial figures are standardized and reported in U.S. dollars (USD) at the time of collection.

• Organizations are classified in segments according to the Cyberflow framework.

• Details such as lead investors, deal amounts, or deal stages may be revised after initial release.

• If you notice any inaccuracies, please let us know so we can correct them.

Disclaimer

This post and the information presented are intended for informational purposes only. While certain information contained herein has been obtained from sources believed to be reliable, neither the author nor any of his employers or their affiliates have independently verified this information, and its accuracy and completeness cannot be guaranteed. Accordingly, no representation or warranty, express or implied, is made as to, and no reliance should be placed on, the fairness, accuracy, timeliness or completeness of this information. The author and affiliated persons and companies assume no liability for this information and no obligation to update the information or analysis contained herein in the future.

My name is Aleix Pérez, and I'm an early-stage tech VC investor based in Barcelona, with a deep focus on AI, cybersecurity, software and data infrastructure. You can reach out to me at aleixperez@me.com for any inquiries.

💰 Funding rounds in December 2025:

🤝 Exits and acquisitions in December 2025:

Data Methodology and Sources

• Grants and secondaries are excluded.

• Service and consulting-focused companies are excluded.

• Financial figures are standardized and reported in U.S. dollars (USD) at the time of collection.

• Organizations are classified in segments according to the Cyberflow framework.

• Details such as lead investors, deal amounts, or deal stages may be revised after initial release.

• If you notice any inaccuracies, please let us know so we can correct them.

Disclaimer

This post and the information presented are intended for informational purposes only. While certain information contained herein has been obtained from sources believed to be reliable, neither the author nor any of his employers or their affiliates have independently verified this information, and its accuracy and completeness cannot be guaranteed. Accordingly, no representation or warranty, express or implied, is made as to, and no reliance should be placed on, the fairness, accuracy, timeliness or completeness of this information. The author and affiliated persons and companies assume no liability for this information and no obligation to update the information or analysis contained herein in the future.

My name is Aleix Pérez, and I'm an early-stage tech VC investor based in Barcelona, with a deep focus on AI, cybersecurity, software and data infrastructure. You can reach out to me at aleixperez@me.com for any inquiries.

💰 Funding Announcements this month:

49 rounds were announced last month with $1.3B+ flowing into cybersecurity

🤝 Exit Announcements this month:

9 acquisitions were announced last month with undisclosed values

Data Methodology and Sources

• Bank debt, grants, secondaries and other non-equity rounds are excluded.

• Service and consulting-focused companies are excluded.

• Financial figures are standardized and reported in U.S. dollars (USD) at the time of collection.

• Organizations are classified in segments according to the Cyberflow framework.

• Details such as lead investors, deal amounts, or deal stages may be revised after initial release.

• If you notice any inaccuracies, please let us know so we can correct them.

Disclaimer

This post and the information presented are intended for informational purposes only. While certain information contained herein has been obtained from sources believed to be reliable, neither the author nor any of his employers or their affiliates have independently verified this information, and its accuracy and completeness cannot be guaranteed. Accordingly, no representation or warranty, express or implied, is made as to, and no reliance should be placed on, the fairness, accuracy, timeliness or completeness of this information. The author and affiliated persons and companies assume no liability for this information and no obligation to update the information or analysis contained herein in the future.

In a world of multi-cloud architectures, container fleets, thousands of microservices, and continuous deployment, the old model of “scan → triage → ticket to IT or dev” is collapsing under its own weight.

This is the environment fueling a wave of innovation in auto-remediation — tools that don’t just detect vulnerabilities, but automatically or semi-automatically fix/remediate them. To make sense of the expanding vendor ecosystem, we’ve created an AppSec Auto-Remediation Market Map, categorizing companies into Incumbents, Challengers, and the new generation of LLM-native Auto-Fixers.

📩 Building in the space? We’d love to hear from you! Reach out Aleix Perez (aperez@caixacapitalrisc.es)

Subscribe now

1. Why Now? The Remediation Bottleneck

Five forces are converging to make remediation automation not just attractive — but necessary:

Backlog Explosion

Vulnerability volumes keep rising as cloud, container, serverless, and IoT adoption expands the attack surface. Traditional scanning and ticketing can’t keep pace.

Speed of Exploitation

Many vulnerabilities are exploited within days or even hours of public disclosure. Remediation velocity — not just detection velocity — now determines breach risk.

Human-Capacity Constraints

Security, operations, and development teams are already overstretched. Manual patching and ticket routing consume scarce talent that organizations can’t scale linearly.

Technological Enablers

APIs, workflow engines, patch-automation tools, and now generative AI make automated analysis and code-level remediation increasingly feasible. Continuous deployment pipelines also create natural insertion points for automated fixes.

Governance & Risk Pressure

Boards, CISOs, insurers, and regulators are asking harder questions: Which vulnerabilities were fixed? How long did it take? Which were auto-remediated?
Visibility and auditability have become mandatory.

The result: remediation is transitioning from “nice to have” to “must-have.” And vendors are racing to define the next generation of AppSec.

2. Mapping the Auto-Remediation Landscape

The market has evolved in clear waves, each marking a leap in automation and intelligence. Our map organizes vendors into three eras:

Wave 1 — The Incumbents (Pre-LLM AppSec Foundations)

Companies: Snyk, GitHub (Advanced Security), Veracode, Checkmarx

These companies built the foundation of modern AppSec: scanners, dependency analysis, and developer-friendly workflows. Their strength lies in detection, prioritization, and code insights — but fixes typically still require human involvement.

They are the classic “remediation helpers”: excellent visibility, strong platform adoption, but not yet fully autonomous remediation.

Wave 2 — The AppSec Challengers (Pre-LLMs, 2015-2022)

Companies: Semgrep, OX Security, Moderne, Lineaje, Apiiro, Arnica Security, Tromzo, ActiveState, Qwiet AI, Phoenix Security, Tamnoon, Cycode, Vicarius, Codacy, Seemplicity

This wave consists of the pre-LLM innovators that pushed AppSec beyond basic scanning and into the era of context, prioritization, and workflow automation.

These companies were built before generative AI became mainstream, relying instead on static analysis, heuristics, ML classifiers, graph analysis, and rule-based engines. They are not LLM-native, but many are now layering AI on top of their original platforms or fully pivoting their products to become LLM-native.

They bridge the gap between detection and automation by focusing on:

1. Risk Prioritization & Correlation

They analyze vulnerabilities across multiple scanners (SAST, SCA, IaC, cloud, container, supply chain) and prioritize them using business context, exploitability, reachability, asset value, and code ownership.

2. Workflow Orchestration & Consolidation

They integrate findings across tools, reduce noise, group duplicates, map issues to teams, and push fixes into developer backlogs.

3. Early Automation & Assisted Remediation

Some vendors began offering code suggestions, dependency updates, secure build pipelines, or workflow-driven remediation steps — but not yet full autonomous code fixing.

• Automated dependency patching: ActiveState

• Script-based remediation & patch automation: Vicarius

• Targeted code-level fixes: Semgrep, Arnica, Apiiro

• Large-scale refactoring & modernization: Moderne

4. Developer-Centric Security

Many platforms in this group were built to integrate directly with Git, CI/CD, or developer workflows — reducing friction compared to traditional AppSec.

Wave 3 — The Auto-Fixers / AI AppSec Engineer (LLM-Native, 2023–Now)

Companies: Seal Security, Aikido Security, Maze AI, Nullify, Pixee, Corgea, Kodem, Backline, Mobb, Depthfirst, Logicstar, Symbiotic Security, Zeropath, Amplify Security, Arvion, Incribo, Veribee, DevArmor, Interfere

Wave 3 represents the first true generation of LLM-native security platforms — vendors founded or reshaped after 2023, built from the ground up around generative AI, automated code remediation, and autonomous fix workflows.
Where Wave 2 optimized “what to fix,” Wave 3 focuses on fixing it, automatically, safely, and at scale.

These companies, also named AI AppSec Engineers, move vulnerability management from assisted to autonomous by focusing on:

1. AI-Generated Fixes & Patch Creation

These tools generate code or configuration fixes directly from scanner findings — including SAST, SCA, IaC, container scans, and cloud misconfigurations. Fixes are delivered as ready-to-merge PRs, automated patches, or directly applied changes.

2. Autonomous Remediation Pipelines

Beyond generating fixes, many Auto-Fixers automatically apply, test, and validate patches across repositories, services, and environments. They often integrate rollback mechanisms and guardrails to ensure safe application.

3. Cross-Domain Coverage

Auto-Fixers often support multiple scanners via a unified Fix API, handling vulnerabilities across first-party code, dependencies, containers, IaC, cloud misconfigurations, and OS packages.

4. Developer-Minimal or Developer-Optional Workflows

Fixes appear directly in GitHub/GitLab pull requests, CI/CD pipelines, and SCM workflows — requiring minimal human approval. Many tools support 1-click fixes, bulk remediation, and hands-free workflows for high-confidence patterns.

5. Early-Stage Innovation & Rapid Expansion

The Auto-Fixer category is young, fast-moving, and attracting strong venture interest. Most companies in this wave were founded post-2023 or pivoted to LLM-native remediation after the generative AI inflection point.

They represent the emerging “99% Auto-Fix” vision for remediation.

Vendor Differentiators to Watch

• Scanner & Language Coverage (SAST, SCA, IaC, cloud-native; Java, Python, JS, Go, etc.)

• Depth of Fix Automation (Fix suggestions → ready-to-merge PRs → autonomous patching & validation)

• Technical Approach (Rule-based, LLM-based, or hybrid deterministic pipelines)

• Safety and Guardrails (Rollback mechanisms, approval workflows, audit logs)

• Business-Risk Alignment (Fixing what actually reduces organizational risk)

• DevOps Integration Depth (Git, CI/CD, ServiceNow, Jira — enabling true shift-left remediation)

What Ultimately Matters: Auto-Fix Accuracy, Autonomy, Speed & % Fully Auto-Fixed

At the end of the day, this is the metric that separates real Auto-Fixers from noise:

How accurately, safely, quickly, and autonomously the platform can fix vulnerabilities — and what percentage of your findings it can remediate end-to-end without human intervention.

High accuracy + autonomy + speed + coverage = the foundation of the next generation of AppSec platforms.

Conclusion

The market’s evolution is now clear:
Incumbents built the detection and AppSec ecosystem.
Challengers introduced workflow and early automation.
Auto-Fixers — LLM-native Code-Fixers — are redefining the category by closing vulnerabilities autonomously.

The shift from “scan → ticket → fix” to “scan → prioritise → auto-remediate (with guardrails)” is already underway. Our vendor map captures this moment, but the real story is the acceleration toward higher automation, developer integration, and business-risk-aware remediation.

Enterprises that embrace auto-remediation early will dramatically increase remediation velocity, reduce backlogs, and free developers from the endless tax of manual security fixes.

As vulnerability volume accelerates, one thing is now clear:
it’s no longer enough to detect and report — you must fix, and you must fix fast.
The era of automated remediation has arrived, and the companies on this map are building its foundation.

📩 Building in the space? We’d love to hear from you! Reach out Aleix Perez (aperez@caixacapitalrisc.es)

My name is Aleix Pérez, and I'm an early-stage tech VC investor based in Barcelona, with a deep focus on AI, cybersecurity, software, and data infrastructure. You can reach out to me at aleixperez@me.com for any inquiries.

💰 Funding Announcements this month:

46 rounds were announced last month with $1.15B flowing into cybersecurity

🤝 Exit Announcements this month:

18 acquisitions were announced last month with a total value of $4.2B

Data Methodology and Sources

• Bank debt, grants, secondaries and other non-equity rounds are excluded.

• Service and consulting-focused companies are excluded.

• Financial figures are standardized and reported in U.S. dollars (USD) at the time of collection.

• Organizations are classified in segments according to the Cyberflow framework.

• Details such as lead investors, deal amounts, or deal stages may be revised after initial release.

• If you notice any inaccuracies, please let us know so we can correct them.

Disclaimer

This post and the information presented are intended for informational purposes only. While certain information contained herein has been obtained from sources believed to be reliable, neither the author nor any of his employers or their affiliates have independently verified this information, and its accuracy and completeness cannot be guaranteed. Accordingly, no representation or warranty, express or implied, is made as to, and no reliance should be placed on, the fairness, accuracy, timeliness or completeness of this information. The author and affiliated persons and companies assume no liability for this information and no obligation to update the information or analysis contained herein in the future.

My name is Aleix Pérez, and I'm an early-stage tech VC investor based in Barcelona, with a deep focus on AI, cybersecurity, software, and data infrastructure. You can reach out to me at aleixperez@me.com for any inquiries.

I hope you enjoy the read!

💰 Funding Announcements this month:

48 rounds were announced last month with $942M+ flowing into cybersecurity

🤝 Exit Announcements this month:

20 acquisitions were announced last month with a total value of $1.8B

Data Methodology and Sources

• Debt, grants, secondaries and other non-equity rounds are excluded.

• Service and consulting-focused companies are excluded.

• Financial figures are standardized and reported in U.S. dollars (USD) at the time of collection.

• Organizations are classified in segments according to the Cyberflow framework.

• Details such as lead investors, deal amounts, or deal stages may be revised after initial release.

• If you notice any inaccuracies, please let us know so we can correct them.

Disclaimer

This post and the information presented are intended for informational purposes only. While certain information contained herein has been obtained from sources believed to be reliable, neither the author nor any of his employers or their affiliates have independently verified this information, and its accuracy and completeness cannot be guaranteed. Accordingly, no representation or warranty, express or implied, is made as to, and no reliance should be placed on, the fairness, accuracy, timeliness or completeness of this information. The author and affiliated persons and companies assume no liability for this information and no obligation to update the information or analysis contained herein in the future.

My name is Aleix Pérez, and I'm an early-stage tech VC investor based in Barcelona, with a deep focus on AI, cybersecurity, software, and data infrastructure. You can reach out to me at aleixperez@me.com for any inquiries.

I hope you enjoy the read!

💰 Funding Announcements this month:

22 rounds were announced last month with $427M+ flowing into cybersecurity

Companies of the month:
🥇 Megarounds: Ontic (Lukas Quanstrom)
🚀 Top Scaleups: 1Kosmos (Hemen R. Vimadalal), Seemplicity (Yoran Sirkis)
⭐ Rising Stars: Archestra.AI (Matvey Kukuy), WiseBee (Stoyan Stoyanov), HuntBase (Tyler Oliver)

🤝 Exit Announcements this month:

15 acquisitions were announced last month with a total value of $26B+

Data Methodology and Sources

• Debt, grants and other non-equity rounds are excluded.

• Funding rounds under $1 million are excluded.

• Service and consulting-focused companies are excluded.

• Financial figures are standardized and reported in U.S. dollars (USD) at the time of collection.

• Organizations are classified in segments according to the Cyberflow framework.

• Details such as lead investors, deal amounts, or deal stages may be revised after initial release.

• If you notice any inaccuracies, please let us know so we can correct them.

Disclaimer

This post and the information presented are intended for informational purposes only. While certain information contained herein has been obtained from sources believed to be reliable, neither the author nor any of his employers or their affiliates have independently verified this information, and its accuracy and completeness cannot be guaranteed. Accordingly, no representation or warranty, express or implied, is made as to, and no reliance should be placed on, the fairness, accuracy, timeliness or completeness of this information. The author and affiliated persons and companies assume no liability for this information and no obligation to update the information or analysis contained herein in the future.

💸 Funding Announcements in Cybersecurity:

48 rounds were announced last month with $1.1b+ flowing into cybersecurity

Data Methodology and Sources

• Debt, grants and other non-equity rounds are excluded.

• Funding rounds under $1 million are excluded.

• Service and consulting-focused companies are excluded.

• Financial figures are standardized and reported in U.S. dollars (USD) at the time of collection.

• Organizations are classified according to the Cyberflow framework.

• Details such as lead investors, funding amounts, or deal stages may be revised after initial release.

• If you notice any inaccuracies, please let us know so we can correct them.

Disclaimer

This post and the information presented are intended for informational purposes only. While certain information contained herein has been obtained from sources believed to be reliable, neither the author nor any of his employers or their affiliates have independently verified this information, and its accuracy and completeness cannot be guaranteed. Accordingly, no representation or warranty, express or implied, is made as to, and no reliance should be placed on, the fairness, accuracy, timeliness or completeness of this information. The author and affiliated persons and companies assume no liability for this information and no obligation to update the information or analysis contained herein in the future.

Thanks for reading Cyberflow! Subscribe for free to receive new posts and support my work.

💸 Funding Announcements in Cybersecurity:

42 rounds were announced last month with $1.3b+ flowing into cybersecurity

Data Methodology and Sources

• Debt, grants and other non-equity rounds are excluded.

• Funding rounds under $1 million are excluded.

• Service and consulting-focused companies are excluded.

• Financial figures are standardized and reported in U.S. dollars (USD) at the time of collection.

• Organizations are classified according to the Cyberflow framework.

• Details such as lead investors, funding amounts, or deal stages may be revised after initial release.

• If you notice any inaccuracies, please let us know so we can correct them.

Disclaimer

This post and the information presented are intended for informational purposes only. While certain information contained herein has been obtained from sources believed to be reliable, neither the author nor any of his employers or their affiliates have independently verified this information, and its accuracy and completeness cannot be guaranteed. Accordingly, no representation or warranty, express or implied, is made as to, and no reliance should be placed on, the fairness, accuracy, timeliness or completeness of this information. The author and affiliated persons and companies assume no liability for this information and no obligation to update the information or analysis contained herein in the future.

💸 Funding Announcements in Cybersecurity:

33 rounds were announced last month with $1.1b+ flowing into cybersecurity

Data Methodology and Sources

• Debt, grants and other non-equity rounds are excluded.

• Funding rounds under $1 million are excluded.

• Service and consulting-focused companies are excluded.

• Financial figures are standardized and reported in U.S. dollars (USD) at the time of collection.

• Organizations are classified according to the Cyberflow framework.

• Details such as lead investors, funding amounts, or deal stages may be revised after initial release.

• If you notice any inaccuracies, please let us know so we can correct them.

Disclaimer

This post and the information presented are intended for informational purposes only. While certain information contained herein has been obtained from sources believed to be reliable, neither the author nor any of his employers or their affiliates have independently verified this information, and its accuracy and completeness cannot be guaranteed. Accordingly, no representation or warranty, express or implied, is made as to, and no reliance should be placed on, the fairness, accuracy, timeliness or completeness of this information. The author and affiliated persons and companies assume no liability for this information and no obligation to update the information or analysis contained herein in the future.