AI Has Changed the Economics of Hacking: What Changes for Businesses After Black October - DEV Community

Posted on Nov 12 • AI-assisted
October's wave of AI-enabled cybersecurity incidents have been termed by some "Black October" and exposed a growing challenge for software businesses: vulnerabilities that have survived years of development and scrutiny are now being uncovered at unprecedented speed.
Three developments from October illustrate the assumptions businesses need to revisit:
The implications go beyond cybersecurity teams. Software that was considered sufficiently scrutinized may no longer meet the same standard of security. And for businesses that operate on technology, this raises a more fundamental question: Are the assumptions underpinning how we build, buy and secure software still fit for purpose?
Examining unfamiliar code, identifying weaknesses and developing working exploits once required significant time and specialist expertise.
AI accelerates each of these steps, making more targets worth investigating and sophisticated attacks more accessible. Automated scanners can search large volumes of code, while coding agents can help identify vulnerabilities and develop ways to exploit them.
Some open-weight models themselves have undergone abliteration: modifications to internal representations that remove the built-in refusal mechanisms and safety guardrails without needing to retrain them. This does not add expertise or guarantee successful attacks, but it can remove friction that might otherwise interrupt malicious activity. (Research paper)
Together, automation and model availability weaken the assumption that attacking a company will require more effort than an adversary is willing to invest.
The silver lining is that AI changes the economics of defense too.
Businesses can use the same advances to inspect code, test applications, investigate findings, and verify fixes. Hosted and locally deployed models provide options when legitimate security testing requires keeping sensitive code within the company's environment.
AI is changing the economics of software development, but the opportunity isn't simply to build more software, faster. It's to rethink how software is built, maintained and secured in the first place.
For growing companies, this means treating security as a core part of the development process rather than a problem to solve once the business scales. It also means questioning whether the complexity they've accumulated through years of software dependencies and third-party tools is still necessary.
For companies with established software infrastructure, the priority is different: understanding what you already have and where there could be risk. Review software dependencies, open-source libraries and third-party integrations for vulnerabilities, outdated components and unnecessary access. Years of accumulated technology can create complexity that is difficult to see, let alone secure.
At e52, we're interested in what becomes possible when AI-driven development is paired with more rigorous engineering: simpler systems, fewer dependencies and security built in from the start. We're also hiring engineers and researchers interested in building systems that are designed to withstand attack. If that's the kind of work you want to do, we'd love to hear from you.
On point and apropos considering today's Microsoft outages. Not that the outages are security related but it demonstrates the fragility of the technology that we use and rely on every day. The too small to fail paradigm is now in the past. Being able to hide isn't an option anymore. Thank you for sharing these insights.
really true, our coding agents are way ahead of our current security, it just cant keep up as of now,
The account and build-system examples make the operational impact concrete. A practical follow-up for teams is to rehearse secret rotation and CI recovery before an incident: who can revoke credentials, how quickly builds can be restored, and whether affected deployments are traceable. Those runbooks turn a disclosure into a testable control.
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