AI is already changing how Managed Security Service Providers (MSSPs) operate. It has the ability to analyse security telemetry faster than humans, automate repetitive workflows, prioritise alerts, and accelerate investigations at a scale that security teams simply can’t match manually.

This matters a great deal because MSSPs are under increasing commercial and operational pressure. Customers are expecting faster detection and response times, along with stronger resilience and clearer evidence that their provider can keep pace with evolving threats.

The skills gap

At the same time, MSSPs are facing skills shortages, expanding attack surfaces, cost pressure, and growing operational complexity. Many providers are being asked to deliver significantly more output without growing headcount at the same rate. That is why AI adoption inside MSSPs is moving quickly from experimentation to operational deployment.

However, despite the hype surrounding autonomous security operations, AI is not removing the need for skilled cybersecurity professionals. In reality, it is actually making human expertise more important.

The future of cybersecurity is not “human-out-of-the-loop” - it is “human-on-the-loop.” In practice, that means analysts are not manually approving every automated action, but they are setting guardrails, reviewing high-risk decisions, investigating anomalies, and knowing when to escalate or override AI-led workflows. 

AI is great at identifying patterns, correlating data, and handling high-volume analysis. It can reduce noise, speed up investigations and automate initial response actions. But cybersecurity is rarely a purely technical problem. Security incidents are ambiguous, fast-moving, and highly contextual. They involve business risk, incomplete information, and constantly evolving attacker behavior.

While AI will help security teams move faster, it can’t fully understand organizational context, make nuanced judgment calls or take accountability during high-pressure incidents.

Humans and AI working together

“Human-on-the-loop” is even more important when you consider that the attackers themselves are using AI-enabled techniques. Our Cybersecurity Workforce Intelligence Report found that AI penetration testing is now the fourth-highest global cybersecurity training interest among more than 702,000 cybersecurity professionals worldwide.

The report also identified a rapidly growing focus on prompt injection, machine learning model exploitation, and agentic AI hijacking. These are not theoretical concerns. They reflect how quickly AI-related attack surfaces are evolving and how actively the cybersecurity community is adapting to them.

For MSSPs, the reality is that AI systems themselves are becoming part of the attack surface. Security teams are no longer simply protecting infrastructure, endpoints, and cloud environments. They also need to understand how AI systems behave, how they can be manipulated, and where automated systems can fail. This is why human oversight is still essential.

AI can accelerate detection, but experienced analysts are still needed to validate findings, investigate anomalies, identify false positives, challenge assumptions, and intervene when automation makes the wrong decision. Without that oversight, organizations will become dangerously overdependent on systems they do not fully understand or validate.

The threat landscape

Attackers do not behave predictably, and real-world incidents rarely follow neat playbooks. Threat actors adapt quickly, and they exploit gaps between systems and target operational weaknesses that automated tooling on its own may not recognize. So, in the middle of complex incidents, human intuition, communication and decision-making still matter enormously.

Changes in cybersecurity operations are also changing the types of skills MSSPs need within their teams. Hack The Box’s research highlights growing overlap between offensive and defensive skill development. Defensive practitioners are engaging in offensive training, and offensive specialists are also developing defensive capabilities.

This is all part of a wider cybersecurity industry shift toward more integrated operational models where security teams continuously test, validate, and improve resilience rather than operating in isolated silos. In practice, this means MSSPs need to employ professionals who understand both how attacks work and how defensive systems respond under real-world conditions.

The strongest MSSPs are unlikely to be those who simply deploy and operate tools. They will be the ones with analysts who have the skills to work alongside AI systems, validating outputs, interpreting context, and adapting quickly when automated systems fail or threats evolve unexpectedly.

A shift in focus

MSSPs need to think differently about workforce development. The skills challenge is no longer just about hiring more people. It is about building adaptable teams capable of operating effectively in increasingly automated and agentic environments.

As AI agents take on more security tasks, MSSPs will need professionals who understand not just alerts and incidents, but delegation, supervision, and control. This is why continuous, hands-on training remains a protected investment for mature MSSPs, even when budgets are under pressure. 

For MSSPs, this has direct commercial implications. Customers are looking for providers that can demonstrate resilience, operational maturity and practical expertise rather than simply deploying more security tools. As AI becomes part of managed security delivery, customers will increasingly expect MSSPs to prove readiness: how teams perform in realistic scenarios, how AI outputs are validated, and how quickly analysts can intervene when automation gets it wrong.

The strongest MSSPs will not be those that simply add AI features fastest. They will be the ones that combine AI-driven efficiency with skilled human oversight, realistic training, and measurable cyber readiness.

AI will absolutely reshape cybersecurity operations. But the MSSPs that benefit most will be those that use AI to scale efficiency internally while preserving human expertise. They will be the providers whose teams know how to question AI, validate it, and step in when it matters most.