Every year, analyst firms publish predictions, and most of us skim them and move on. One from Forrester’s 2027 cybersecurity and risk report stuck with me because it describes a problem developers are already living with. Forrester predicts that an AI-generated bug or regression will cause a global outage across multiple industries, on the same scale as the July 2024 outage.
forrester
The cause it names isn’t “AI writes bad code.” It’s the mismatch between how fast vulnerabilities are found and patched and how much testing capacity exists. That is a pipeline problem, and pipelines are something we can fix.
forrester
Generation got cheap, verification didn’t
AI assistants have made writing code very fast. A function that took twenty minutes now takes twenty seconds, and a patch for a newly disclosed vulnerability can be drafted before the coffee cools.
Reading, reviewing, testing, and deploying that code still happen at human speed. Review is still done by people with finite attention, and staging environments still differ from production in the ways that hurt.
Forrester’s framing is that the pace of AI-assisted development and patching will outstrip testing capacity, leading to a major outage from faulty AI-generated code. Press coverage of the report adds that the pressure to distribute patches quickly will overwhelm software engineers, creating a need to rely even more on AI. That’s a feedback loop. We use AI to keep up with the volume AI created.
Your throughput is now limited by how fast you can trust code, not how fast you can write it.
The 2024 comparison is worth reading carefully
The July 2024 outage Forrester points to was the CrowdStrike incident, where a faulty update crashed a huge number of Windows machines worldwide. As far as I know, AI had nothing to do with it. It was an ordinary software quality failure with a very wide blast radius.
That’s the point. Forrester isn’t predicting a new kind of failure. It’s predicting the same failure at higher frequency, because the volume of change keeps rising while the safety net stays the same size. Their advice is that organizations need to get better at rapidly rolling back unreliable vendor patches.
forrester
Notice that the recommendation is about recovery, not prevention. Prevention will never be perfect, so the question is how fast you can undo a mistake.
What I’d change on Monday
None of this requires exotic tooling.
1. Treat rollback as a feature, not an emergency procedure. If you’ve never rehearsed reverting a bad release, you don’t have a rollback plan. You have a hope. Practice it the way you’d practice a fire drill.
2. Stage everything that touches many machines. Canary releases, percentage rollouts, and kill switches exist because the worst bugs only show up at scale. A change that goes to everyone at once is an experiment on all your users.
3. Be honest about where AI code is flowing. Know which parts of your codebase were mostly machine-generated and which were reviewed line by line. You don’t need to ban anything. You need to know where your thinnest review coverage is.
4. Make tests the bottleneck on purpose. If your tests can’t keep pace with your generation speed, let the tests set the pace. A suite that catches regressions is worth more than a model that writes features faster.
5. Have a dependency story. Many of the outages that go global come from vendor updates, not your own code. Know which vendors can push changes to your production systems without your approval.
The other prediction: accountability
The same report makes a second prediction that developers should care about. It says an AI negligence lawsuit will force a CEO’s exit or a leadership change, with US accountability for AI still fragmented but heading toward the courts. One example from the broader conversation is the Mobley v. Workday case, where an age discrimination lawsuit over AI hiring software also named dozens of companies that used the tool as co-defendants.
Predictions 2027: It’s Going To Be Another “Hold My Beer” Kind Of Year For Security And Risk
Explore Forrester’s 2027 cybersecurity predictions, from AI accountability and outages to NIS 2 risk, and prepare for disruption.
forrester.com
The lesson for engineers is that “the model did it” won’t work as a defense. If you deploy a system, you own its behavior, and organizations will increasingly need documentation, logs, and decision records that show who approved what. Audit trails and design docs, which many teams treat as optional, start to look like legal infrastructure.
A fair caveat
Predictions are not forecasts you can bank on. Analyst firms make bold calls partly because bold calls get attention, and Forrester itself has urged leaders to focus on the operational disciplines needed for adoption rather than another cycle of AI alarmism. I’d take the same stance. Whether the big outage lands in 2027 or never, the underlying gap between how fast we generate code and how fast we verify it is already here.
Forrester urges firms to prioritise practical AI governance
Enterprises face mounting risks from AI outages and hidden governance costs as Forrester says day-to-day operational needs now outrank frontier fears.
itbrief.co.nz
The takeaway
Speed was never the hard part of software. The hard part is knowing that what you shipped does what you think it does, and being able to take it back when it doesn’t. AI made the easy half much easier. The teams that do well in 2027 will be the ones that put the saved time into the hard half.
So here’s a question to bring to your next retro: if the last change you shipped turned out to be wrong, how long would it take to undo it? If you can’t answer confidently, that’s your roadmap.
Sources:
Forrester, “Predictions 2027: It’s Going To Be Another ‘Hold My Beer’ Kind Of Year For Security And Risk” (Forrester blog). Basis for the AI-generated-bug outage prediction, the testing-capacity mismatch, the rollback advice, and the AI negligence lawsuit prediction.
https://forrester.com/blogs/predictions-2027-its-going-to-be-another-hold-my-beer-kind-of-year-for-security-and-risk
Forrester press release, “Forrester’s 2027 Technology And Security Predictions: AI Will Account For 20% Of All Business Technology Spend.” Basis for the claim that AI-assisted development and patching will outpace testing capacity.
https://www.forrester.com/press-newsroom/forresters-2027-technology-and-security-predictions-ai-will-account-for-20-of-all-business-technology-spend/
Professional Security, “AI, cyber predictions.” Basis for the point that patch-distribution pressure will push engineers toward relying more on AI.
https://professionalsecurity.co.uk/news/case-studies/ai-cyber-predictions/
IT Brief US, “AI lawsuits risk surges by 2027, warns XFactorAi CEO.” Basis for the Mobley v. Workday example.
https://itbrief.news/story/ai-lawsuits-risk-surges-by-2027-warns-xfactorai-ceo
IT Brief New Zealand, “Forrester urges firms to prioritise practical AI governance.” Basis for the “operational discipline over alarmism” caveat.
https://itbrief.co.nz/story/forrester-urges-firms-to-prioritise-practical-ai-governance
Top comments (1)
The point that review still runs at human speed is the part I’d underline, because human review has well-documented limits. The vigilance literature going back to Mackworth’s radar-watch studies in the 1940s shows detection falling off over sustained monitoring. The Cisco code review case study (run with SmartBear, so read it with that in mind) is often cited for finding that reviewers catch less once they go past a few hundred lines in a sitting. If AI multiplies the volume of diffs while reviewer attention stays fixed, the safety net doesn’t just stay the same size. Per line of code, it gets thinner.
There’s a second effect worth naming: automation bias. Parasuraman and Manzey’s 2010 review found that people tend to over-trust output from automated systems and under-check it, and that expertise doesn’t fully protect against it. Code that looks clean, compiles, and comes from a tool we use every day is exactly where that bias would show up. So your point 3, knowing where machine-generated code lives, may matter beyond review coverage. It also tells you where reviewers are most likely to be skimming without realizing it.
That’s why I think the framing around rollback is the strongest part of the piece. Prevention depends on sustained human attention, which the research says is fragile. Recovery depends on systems you can rehearse ahead of time. Shifting effort from the fragile part to the rehearsable part looks less like pessimism and more like designing around how people actually work.