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AI is moving from proof-of-concept into everyday security operations. In many SOCs, it is now used to cut down alert noise, guide analysts during investigations, and speed up incident response. What was once seen as experimental technology is starting to deliver results that CISOs can measure.
Some of this has been in place for years. Machine learning already powers many threat detection engines and behavioral analytics tools. But the recent wave of GenAI has opened new doors. CISOs are now weighing where these tools can help, where they need guardrails, and what it means for their teams.
Fewer alerts, faster triage
Security teams are used to drowning in alerts. Most are false positives, some are low risk, only a few matter. AI is helping to cut through this mess.
Vendors have been building machine learning models to sort and score alerts. These tools learn over time which signals matter and which can be ignored. When tuned well, they can bring alert volumes down by more than half. That gives analysts more time to look into real threats.
GenAI adds something new. Instead of just ranking alerts, some tools now summarize what happened and suggest next steps. One prompt might show an analyst what an attacker did, which systems were touched, and whether data was exfiltrated. This can save time, especially for newer analysts.
Erez Tadmor, Field CTO at Tufin, said AI also plays a role in reducing time spent troubleshooting network access issues. He described a case where a DevOps team deploying a new microservice in Kubernetes could not reach its backend database in AWS. “Rather than reviewing VPC settings, security groups, and network policies manually, they asked an AI assistant, ‘Why can’t Service A reach Database B?’ The AI traced the entire path, identified a stale Network ACL entry blocking the connection, and recommended a minimal, safe change, resolving the issue in minutes instead of hours or days.” He added that AI can also map how attackers might have moved laterally, even across complex zero trust or hybrid environments, which allows faster, more confident decision-making.
“A healthcare CTO saved tens of thousands in HIPAA compliance consulting while mitigating a business email compromise attack in 4 hours versus days of manual response. A large county government avoided hundreds of thousands in consulting costs, completing comprehensive security analysis in minutes rather than weeks,” said Josh Ray, CEO of Blackwire Labs. “In some cases, Fortune 100 firms have cut intelligence analysis time by half and are even seeing reduced cyber insurance premiums from advanced analytics.”
AI copilots in the SOC
The idea of an “AI copilot” is gaining traction. These tools act like virtual assistants for security teams. They can answer questions, help with investigations, and even write scripts or queries.
For example, an analyst might ask the copilot, “Show me recent activity from this IP address.” The AI can pull the right data and explain what it finds in plain language. This lowers the barrier to entry for junior staff and helps everyone work faster.
Some tools also help automate routine tasks, like blocking IPs, isolating machines, or resetting accounts. This speeds up response once a threat is confirmed.
“Humans are still an important part of the process. Analysts provide feedback to the AI so that it continues to improve, share environmental-specific insights, maintain continuous oversight, and handle things AI can’t deal with today,” said Tom Findling, CEO of Conifers. “CISOs should start by targeting areas that consume the most resources or carry the highest risk, while creating a feedback loop that lets analysts guide how the system evolves.”
Training the models
One challenge is getting the models to learn from your own environment. Generic models work up to a point, but every company’s network is different.
Vendors are working on ways to tune AI tools with local data. This includes logs, threat history, and playbooks. The more relevant the training data, the better the results. But this also raises questions about data privacy and control, especially if models are hosted in the cloud.
CISOs need to ask how data is used, whether it is shared, and how the model can be audited. Even if governance is not the main focus today, these questions will matter as tools become more embedded.
Impact on the team
One of the biggest changes will be how AI affects security roles. Entry-level analysts may no longer spend all day clicking through dashboards. Instead, they might focus on verifying AI suggestions and tuning the system.
That could be a win, especially in a market where talent is scarce. But it also means teams will need to develop new skills. Knowing how to write prompts, interpret AI output, and fine-tune models will become part of the job.
Most CISOs are still in the testing phase, but early adopters are already seeing improvements in speed and scale. The key is to keep humans in the loop and treat AI as a force multiplier, not a replacement.
What to look for next
The next phase may include AI-driven correlation across tools, better attack simulations, and adaptive response workflows. As products mature, they will get better at understanding context and intent.
CISOs should stay close to these developments but remain practical. Not every task needs AI, and not every product delivers what it promises. But in the right places, these tools can give your team an edge.
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