AI has fundamentally changed cybersecurity. Even the most primitive attackers are now capable of launching attacks at an unprecedented speed, frequency, and level of sophistication. As a result, defenders are under more pressure than ever.
Often, when we talk about AI, we talk about its potential to put people out of work. This is perhaps an understandable concern: AI is getting increasingly good at carrying out many cybersecurity tasks, especially those typically performed by real staff. But that doesn’t mean that it will replace human analysts.
Instead, we should think of AI as a copilot, a partner that accelerates triage, enriches investigations, and supports confident decision-making. Instead of taking the wheel, AI should sit alongside analysts, helping them see more, act faster, and focus on more valuable work.
Why Analysts Need a Copilot
Security teams face an avalanche of alerts, logs, and telemetry. Separating signal from noise is a constant struggle. Analysts typically spend hours sifting through false positives or chasing low-priority events – time they could spend proactively hunting threats or improve defenses.
With an AI copilot, however, they can:
- Triage at Scale: AI tools can filter incoming alerts, correlate related events, and enrich them with threat intelligence, asset data, and identity context. The result? Analysts have a clear view of what’s urgent and what can be pushed to the backburner.
- Accelerate Investigation: AI quickly gathers relevant logs, traces activity to MITRE ATT&CK techniques, and surfaces probable root causes. This means that analysts get the evidence they need without spending hours manually pulling evidence from multiple sources.
- Make Better Decisions: Because AI provides context, probable outcomes, and likely next steps, analysts can make faster, more informed decisions.
Analysts Agree: AI Helps, But it Won’t Replace
According to the Cloud Security Alliance (CSA), security professionals are generally optimistic about AI’s role in security operations. Only 12% believe the technology will fully replace their role, and most see it as an enabler:
- 30% say it will enhance their skill set
- 28% say it will generally support their role
- 24% expect it to take over large portions of their work, freeing them to focus on higher-value tasks
Fortunately, it seems that AI panic hasn’t taken hold of the security community. They understand that while AI can handle the heavy lifting, human judgement, creativity, and intuition remain irreplaceable.
What Human-AI Collaboration Looks Like in Practice
While AI does improve processing speeds in security operations, it does much more than that; it changes the flow of work for analysts. Instead of moving linearly through the triage-investigation-decision cycle, analysts can now work in parallel with AI, sharing the load at every stage.
In a typical scenario, when a suspicious login or lateral movement pattern appears:
- AI leads the initial sweep, collecting telemetry from endpoints, identity systems, and network logs; correlating it with threat intelligence and discarding false positives without human intervention.
- The analyst steps in for judgement calls, reviewing high-priority cases AI has flagged, weighing factors like business impact or operational risk that can’t be inferred from data alone.
- AI prepares the next move, suggesting targeted responses – such as isolating a host, revoking a token, or adjusting firewall rules – as well as providing confidence scores and potential consequences.
- The analyst authorizes and refines, approving, modifying, or rejecting AI’s recommendations, and using that feedback to fine-tune future triage and detection models.
With this approach, organizations create a dynamic feedback loop: the AI becomes sharper with every human decision, and analysts gain faster, cleaner starting points for each investigation.
Some leading platforms, like ReliaQuest, already operate in this way, integrating cross-tool visibility with AI-driven prioritization so that no matter where a threat signal originates, it integrates into a single, context-rich workflow. This ensures that the analyst never has to “hunt for the hunt” and can move straight into meaningful action.
The outcome? Clarity, confidence, and a shift from reactive firefighting to proactive defense.
Looking Ahead: From Assistants to Agents
However, this is only the beginning. AI in cybersecurity is rapidly evolving from a helpful assistant into a more capable copilot – one that can take the controls briefly when speed counts. Three capabilities will define this next chapter:
- Agentic AI: Acts on its own when necessary, then reports back to human analysts. It assesses risk in real time, automatically takes remediation actions, and learns from analyst feedback to refine future actions.
- Hyperautomation: Automates the entire threat lifecycle, ingesting and correlating telemetry from multiple tools, triaging and prioritizing alerts, and executing containment steps, surfacing only what needs human judgement.
- Multi-Agent Systems: These are specialized AI teammates that work across different domains. Endpoint, cloud, identity, and network agents work in parallel, while a central orchestrator combines findings for a complete picture.
However, this still doesn’t mean that AI will replace human analysts. As attackers get faster and more sophisticated, human defenders will still need to be there. AI will become an ever-faster, sharper, and accurate copilot, ready to handle split-second decisions while humans take care of the bigger picture.
Josh is a Content writer at Bora. He graduated with a degree in Journalism in 2021 and has a background in cybersecurity PR. He's written on a wide range of topics, from AI to Zero Trust, and is particularly interested in the impacts of cybersecurity on the wider economy.
The opinions expressed in this post belong to the individual contributors and do not necessarily reflect the views of Information Security Buzz.