The debate just hit a boiling point.
Recent demonstrations of Claude discovering hundreds of vulnerabilities in audited open-source code – combined with research agents like Stanford’s ARTEMIS outperforming traditional approaches – triggered a familiar reaction.
Markets panicked. Billions were wiped off legacy cyber stocks in a single session. But is the fear justified?
Let’s start with the big picture – can AI “vibe code” a cybersecurity platform? Short answer – NO.
Has AI “vibe coded” ANY enterprise grade platform in ANY field? The answer is NO. Then why will cybersecurity enterprise platforms be any different?
Sure – if the task is used by individual developers and the code lives in an open-source repository that has been well documented and refined for decades (think the C compiler or the Firefox browser), LLMs and AI agents can reason through the codebase and even generate replicas that work just as well — sometimes better in certain parts.
But an enterprise platform is a completely different beast.
- These systems are closed source.
- They contain years of operational “scar tissue.”
- Millions of lines of code shaped by real-world deployments.
- Deep integrations, compliance requirements, and workflows tailored to how large enterprises actually operate.
Replicating that with AI today – or even in the near future – simply isn’t realistic. Partly because it’s not just an “intelligence” problem, it’s an “experience and evolution” problem that can only be learnt with real world deployments at scale.
That’s why we haven’t seen a single “vibe-coded” platform emerge that replaces systems like Salesforce, ServiceNow, or CrowdStrike.
Hence, the need for Software is going to remain strong – and is not going away anytime soon.
But does this mean cybersecurity companies won’t get disrupted?
Absolutely not.
We saw the same debate in 2024 when AI models like o1 emerged. People panicked, asking whether AI would replace humans across every job.
The answer quickly became clear: Humans with AI will replace humans without AI.
The same principle applies here.
AI-native cybersecurity platforms will replace legacy cybersecurity platforms.
I write “AI Native” cybersecurity software and not “Cybersecurity Software with AI” – and this is what I think is a fundamental difference that most people are missing.
So what is “Cybersecurity Software with AI”?
It takes 4 lines of code to call an OpenAI API, pushing a PDF Document to get an AI Summary of a PDF – and that is what most legacy cyber software companies are doing – showing a document summary in their software and branding their software as “AI”. But this is the wrong approach. It’s like putting lipstick on a pig.
So, what is “AI Native Cybersecurity Software”?
One that is architected, grounds up, for the new age. Some attributes of what that looks like …
- Starting with data ingestion – storing data in modern Unified Datalake Systems, vectorized
- Deeply embedded LLMs and SLMs for tasks that they are proficient to handle
- Agent Orchestrator selecting the right LLMs to call based on Natural Language Query
- MCP that talks to every API / Tool within a product and the public internet
- Native Inference ability to leverage the LLM training, along with RAG
Couple those capabilities with what “Enterprise Grade” AI means …
- Highly customized agentic workflows designed to navigate the deeply nuanced operational processes of large enterprises at scale.
- GPT-style conversational interaction on your phone, enabling you to query, reason through, and interpret complex data points in seconds.
- Lovable-style dashboards and insights, seamlessly stitching together internal and external knowledge graphs into a unified intelligence layer.
- Enterprise-grade trust and traceability framework ensuring transparency, auditability, and reliability of every output produced by AI agents.
- Flexible data localization capabilities allowing the platform to be deployed securely within the customer’s own cloud environment.
And it goes without saying – every “Cybersecurity Software with AI” company will have a roadmap to become a “AI Native Cybersecurity Software” company in the near future – but the difference is the speed of execution of fighting the inertia of legacy (meaning moving a LOT of customers to a fundamentally new platform), while building the “new” platform, while delivering numbers quarter over quarter. We saw the exact same movie while software moved from “on-prem” to “cloud”.
At SAFE we see this live in action everyday – just last week – I was in a meeting with one of the top 3 banks in the world, and they said what SAFE showed LIVE in our AI Native platform was what the “largest legacy vendor” in our space had planned to deliver at the end of their 2 year roadmap.
So while every software worth their salt will put the AI lipstick to look “AI Pretty” – the rubber meeting the road is for enterprise customers to look under the hood – if it’s a pig or a unicorn of the AI era.
