Kubernetes and native platforms in the cloud have redefined the way we create and execute software. They give us speed, agility, elasticity – the ability to extend in seconds and come back in a few minutes. But the attackers do not care about your CI / CD speed. They do not care about the number of bar graphs that you have optimized or the speed at which you can deploy in production. They care about one thing: what works at the moment.
This is why the visibility of the execution has become the new security line of security. This is where abstractions meet reality. And this is also where traditional defenses fail.
We have seen it again and again. A poorly configured RBAC role which seemed harmless in Git turns into an escalation of privilege on a cluster scale at the time of execution. A container image that has passed static analyzes is exploded by a library operated the day after the exit. Or a supply chain attack injects malicious code into a dependence of confidence which only proves to be the production service.
Build-time checks are necessary. The speed change safety was a big step forward. But it is not enough. The fight is at the time of execution – and we need better visibility and a faster response than humans alone cannot deliver it.
The case of visibility of the execution
The problem is that the complexity of the Cloud-Native works against defenders. We no longer protect a few static servers. We secure thousands of ephemeral containers that turn upwards and downwards through clusters, functions operating during milliseconds in server -free environments, service jerseys directing traffic on dozens of microservices, API speaking to APIs.
Traditional perimeters do not exist here. Your firewall does not see East-West traffic inside your Kubernetes cluster. Your WAF does not know that a POD simply caused a suspicious process that it should not have. Execution is the place where the configuration, the code and the context collide. And that’s where attackers like to hide.
This is why tools like Falco, built on EBPF (Extended Berkeley Package filter), have gained ground. They allow you to capture systems, processing behavior and network events at the time of execution with a minimum of general costs. The service stitches add another layer of telemetry, giving an overview of who is talking to whom. Observability batteries such as metrics, newspapers and traces of Prometheus, Loki and Jaeger.
The signals are there. The challenge is that they produce a data assessment. And this is where AI enters the image.
AI meets the safety of execution
AI is not new in safety – Automatic learning has been used in end and SIEM products for years. But in native cloud environments, the volume, variety and speed of execution data require a new level of automation.
AI can help in three key ways:
- Anomaly detection. The AI models formed on the “normal” execution behavior can detect deviations in process execution, API calls or network flows. For example, if a pod suddenly starts making outgoing calls, it has never made before, or generates a shell inside a container that should be immutable, AI can raise a high confidence alert.
- Automated response. Instead of waiting for a human sorting, the manuals based on AI can isolate a pod, block a suspicious intellectual property or make a deployment in real time. Imagine a world where the detection / response loop is measured in seconds, not hours.
- Contextual enrichment. This is where the LLM shines. Instead of launching raw systems or JSON BLOBS among analysts, AI can generate incident stories: “Pod X in the name space has tried to write to / etc / passwd. It is much more useful than 10,000 newspaper lines.
The sellers are already moving in this direction. WIZ has introduced AI extensions for execution analysis. Aqua Security explores AI to supply the detection of anomalies in its execution protection. The Falco project of the CNCF has been the subject of research incorporating AI / LLMS into the generation of rules and noise reduction. Everyone sees the same point of pain: the visibility of the execution without intelligence is only noise.
Balance: power and risk
But don’t have star eyes. AI also brings risks.
False positives and false negatives are inevitable. An AI system that blocks legitimate traffic in production can cause as much damage as the attack he tries to prevent. And an AI that lacks a subtle feat because it did not “correspond to the model” gives defenders a false feeling of security.
Another challenge is explanation. Security leaders and listeners do not want a black box saying to them “deny this workload” without reasoning. IA -based security must provide evidence, links to policies and links with known executives such as CIS or Miter ATT & CK references. Otherwise, confidence will never materialize.
And let’s not forget the opponent. The attackers already experiment with poisoning models, create entries that induce AI to ignore malicious behavior or overload it with noise.
This is why the right approach is AI as a co -pilot, not the automatic driver. The AI must filter, enrich and recommend – but humans must stay in the loop. Executive safety is too critical to fully outsource a black box.
What it means for safety and platform teams
The implications are important. Observability pipelines must now serve as safety pipelines. The metrics, the traces, the newspapers and the events are not only for the SRES – they are fuel for the defense focused on the AI. The convergence of observability and security is real, and execution is the place where it will occur first.
The platform engineering and safety teams must work together. The visibility of the execution time cannot be a reflection afterwards or a bolted agent. It must be integrated into the clusters, mesh and pipelines. Political workflows as a code and gitops should extend to execution safety controls, the helping the detection and application of the conformity of the drift.
Regulators also pay attention. While the AI makes more decisions, the listeners will require evidence of how these decisions have been made. If your AI in quarantine a workload, can you show why? Can you prove that he has not raped the rules of compliance in the process? Transparency and governance will become as important as detection itself.
Taking Shimmy
The execution time is where the rubber meets the road. If you can’t see it, you can’t secure it. And if you cannot answer in real time, you are already too late.
LLM and AI are not silver balls, but these are the best tools we have to cut the noise and surface what matters. They can help us find the needle in the hay boot, reduce alert fatigue and act faster when the seconds count.
The future of native cloud safety is not humans compared to machines. It is humans and machines that work together – observability fueling AI, AI guiding the answer, humans applying a judgment.
The native world of the cloud does not slow down so that we can resume our breath. The attacks do not stop, so we can scroll the dashboards. The only way to follow is smarter visibility, faster action and confidence in our tools and teams. The AI will not replace the defenders. But defenders who do not use AI can already find themselves exceeded.
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