GenAI in Cybersecurity: Opportunities, Risks, and What Comes Next

In case you missed the thousands of AI headlines, generative AI made phishing and social engineering cheaper, faster, and more convincing — and it shows. Attackers adopted GenAI faster than most organizations could decide to deploy it to defend themselves. Now they’re using it to craft hyper-personalized phishing attacks, spin up mutated malware, and launch campaigns at a scale and speed that would’ve seemed impossible just a few years ago.

Phishing attacks have surged 1,265% since the adoption of generative AI tools, and AI-assisted attacks have increased 72% year over year.  If you’re still in the “we’re evaluating AI” phase, attackers are 10 steps ahead of you. 

Keep reading to see the breakdown of what generative AI actually means for cybersecurity: the opportunities, the risks, what it looks like when you get the deployment right, and why agentic AI is the next step for SOCs that need to move beyond summaries to real execution.

What is Generative AI in Cybersecurity?

Generative AI refers to machine learning models trained on massive datasets to produce new content — text, code, images, and synthetic data. In a cybersecurity context, that capability cuts both ways.

For defenders, generative AI powers smarter alert correlation, faster incident summarization, automated investigation planning, and natural language interfaces that let analysts query complex datasets without writing a line of code. 

For attackers, that same technology generates convincing phishing emails, deepfake audio for social engineering, and malware variants that mutate fast enough to outrun signature-based detection.

Unlike traditional machine learning (which classifies or predicts based on labeled data), generative AI creates. It flags anomalies and synthesizes new intelligence, attack scenarios, and defensive responses in real time. That’s what makes it genuinely disruptive.

How Generative AI Works in Security Systems

Generative AI models (particularly large language models) in cybersecurity can learn patterns from enormous volumes of data, including threat intelligence feeds, incident reports, security documentation, and network logs. Once they’re trained, these models can generate new insights: summarizing what happened in a breach, recommending next steps, or flagging hidden relationships between seemingly unrelated events.

In SOC environments, this means analysts no longer have to stitch context together from five different tools manually. A well-integrated generative AI model enriches alerts with relevant threat intelligence, generates investigative hypotheses, and surfaces the most likely root cause. This means analysts spend their time making decisions rather than hunting for data.

That’s a fundamentally different posture than rule-based detection. It’s not waiting for a known-bad signature to appear. It helps teams interpret ambiguity faster — and move to action with more context.

And while GenAI excels at turning messy security data into clear, actionable output, the industry is beginning to push further — toward agentic AI that doesn’t just inform decisions, but helps execute them.

Top Generative AI Use Cases in Cybersecurity

A 2024 Cloud Security Alliance survey found that 94% of organizations were actively planning or testing generative AI for specific security use cases. Here’s where it’s actually making a difference.

How SOCs Use Generative AI to Automate Threat Detection

Alert fatigue is occurring every second of the day and pushing people to their breaking points. This is both from burnout and from critical threats buried in the overwhelming pool of false positives. However, generative AI changes this. 

Rather than requiring analysts to manually triage every alert, AI-powered platforms automatically correlate alerts, enrich them with contextual threat intelligence, and generate investigation-ready summaries. 

For lower-severity alerts, generative AI can handle much of the investigative legwork — correlating signals, ruling out false positives, and surfacing a clear disposition for the analyst to confirm. Higher-severity cases get escalated with the work already done: evidence gathered, affected assets identified, attack path mapped.

This type of ROI is hard to argue with. IBM’s 2025 Cost of a Data Breach Report found that organizations that extensively use AI SOC automation saved an average of $1.9 million per breach and reduced their breach lifecycle by 80 days. 

Generative AI for Phishing Detection and Adversarial Simulation

Phishing is getting dangerously good. AI-generated phishing emails now achieve a 54% click-through rate. Attackers are using LLMs to personalize emails at scale, stripping out the telltale grammatical errors that filters used to catch.

But defenders are fighting back with their own generative AI. 

• Phishing detection: AI models analyze email content, sender behavior, domain reputation, and contextual signals simultaneously. Torq’s automated phishing investigation and response workflows handle the full lifecycle without analyst intervention for most cases.
• Adversarial simulation: Red teams now use generative AI to simulate realistic attacks before real attackers do. Organizations that train against AI-generated threats are materially better prepared for the real thing.
• Automated threat enrichment: Generative AI enriches every case with relevant threat intel, asset criticality data, and historical incident patterns automatically. Torq’s contextual threat intelligence enrichment is built directly into the Torq AI SOC platform workflow. No more context-switching. Every alert arrives investigation-ready.

Risks and Challenges of Generative AI in Cybersecurity

The same capabilities that make generative AI powerful for defenders make it dangerous in the wrong hands. Whether it’s deepfakes, prompt injections, or sensitive data leakage, there’s two sides to every coin. 

Here’s what the other side looks like:  

• Sophisticated attacks: Deepfakes are no longer a novelty. Attackers use AI-generated audio and video to impersonate executives, authorize fraudulent wire transfers, and bypass identity verification. Meanwhile, AI-powered phishing campaigns target thousands of individuals simultaneously with hyper-personalized content. 93% of cybersecurity professionals expect AI-enabled threats to impact their organization — and most are already feeling it.
• Prompt injection: Prompt injection can cause AI systems to take unauthorized actions, bypass controls, or leak sensitive data. 
• Data poisoning: Data poisoning attacks corrupt AI model training data to degrade detection accuracy or introduce backdoors. 
• AI-specific vulnerabilities: Model theft, adversarial examples, and sensitive data leakage through AI outputs create a new class of risk that traditional security frameworks weren’t designed to handle.

The risks aren’t in the technology. They’re in how you deploy it. These risks are the byproduct of rushing AI deployment without governance, AI guardrails, or training. Get those three things right, and generative AI is one of the most powerful tools in your toolbox. 

Ethical and Compliance Considerations

Running a SOC used to mean managing analysts. Now it means managing AI and being accountable for every action it takes. This means building AI governance into your security program from the start. Here are some key considerations:

Model transparency and auditability: Every automated or AI-driven action should be fully traceable — a clear, logged rationale for every case closed, host quarantined, or incident escalated. Black-box AI in a SOC is a liability. 

Human-on-the-loop controls: Not every decision should be fully automated. High-stakes actions warrant human confirmation. 

Regulatory alignment: There are 59 new AI-related regulations issued in the U.S. in 2024 alone. This is more than double than the prior year. SOC leaders need to ensure their AI deployments meet emerging compliance requirements around data handling, explainability, and model governance. 

Generative AI is the Foundation, Not the Finish Line

Everything above describes what generative AI brings to security operations: faster enrichment, better phishing detection, and investigation-ready summaries. But here’s the part the market is still catching up to: generative AI, on its own, doesn’t close cases. It doesn’t take action. It doesn’t decide what to do next.

Generative AI answers questions. It summarizes. It creates. What it doesn’t do is reason through a multi-step investigation, decide whether to contain a host or escalate to an analyst, and then execute that decision autonomously. That’s the gap — and it’s the gap that separates a SOC with a chatbot from a SOC that actually operates at machine speed.

Getting there requires three capabilities:

1. Agentic AI adds goal-setting, planning, and autonomous execution on top of generative AI’s reasoning. Instead of waiting for an analyst to prompt it at every step, agentic AI investigates an alert end-to-end: gathering context, correlating signals, making a severity determination, and taking the appropriate action — all within defined guardrails. Torq’s AI SOC Analyst, Socrates, operates this way. It doesn’t summarize cases for humans to act on. It acts, and shows its work.
2. Multi-agent systems (MAS) take this further by coordinating specialized AI agents across the case lifecycle. One agent handles enrichment. Another handles user communication. Another handles decisioning and ticketing. They collaborate like a team of analysts — each with a defined role, all orchestrated through a single control plane. This is how Torq AI SOC operates in production today, and it’s the architecture that IDC and GigaOm have validated as the path to the autonomous SOC.
3. Custom AI models trained on security-specific data outperform general-purpose LLMs on every metric that matters in a SOC: detection accuracy, false positive reduction, and contextual reasoning about your environment. General-purpose models hallucinate. Security-tuned models — built on millions of real security events — don’t guess. They reason from evidence. Torq’s AI Agents are built on this principle: specialized, transparent, and trained for security operations.

The organizations still treating generative AI as the destination are simply building a smarter assistant. The organizations treating it as the foundation — and layering agentic AI, multi-agent orchestration, and security-specific models on top — are building an autonomous SOC.

The Autonomous SOC Still Needs You

Even with agentic AI handling the volume, the best security operations will always combine machine speed with human judgment. The SOC doesn’t become analyst-free — it becomes analyst-focused.

Here’s where things are heading:

• Autonomous Tier-1 operations: Routine alert triage, evidence enrichment, and low-severity threat disposition will be fully automated as standard operating procedure. Human analysts will focus on strategic threat hunting, complex incident investigation, and high-stakes decisions that require contextual judgment.
• Human-on-the-loop orchestration: The most effective SOCs will be intelligently hybrid. AI handles the volume; humans handle the nuance.
• Adaptive learning models: The future is AI that learns from every incident, every analyst decision, and every false positive — the shift from automation to genuine operational intelligence.

Meet Torq’s AI SOC

Torq’s AI SOC is built for exactly this moment. It combines Torq Hyperautomation™ with AI-driven workflows so teams can move beyond GenAI summaries to consistent, governed security operations — with auditability built into execution.

At the core is Torq’s AI SOC Analyst, Socrates. Socrates coordinates multiple AI Agents for contextual alert triage, incident investigation, and auto-remediation of Tier-1 tasks. 

For critical threats, Socrates enables analysts to take action faster through natural language human-AI collaboration. And as the market shifts toward custom LLM strategies, Torq supports that evolution by letting teams align AI-assisted tasks to their environment, governance requirements, and operational goals. Socrates plans customized agentic threat investigations and accurately assesses threat impact so Torq’s AI SOC can prioritize responses effectively.

What makes Torq different? It’s the combination of agentic AI reasoning and Hyperautomation — deep integration with your security stack, configurable human-on-the-loop controls, and adaptive workflows built to close over 90% of security cases completely autonomously. 

We’re Not Slowing Down: AI SOC or Die

The future of AI in cybersecurity is here, and it’s not tapping the brakes for anyone. GenAI was step one. Agentic AI and AI agents are step two, because the SOC needs execution at scale, not just better writing.

SOC leaders who move fast and deploy AI thoughtfully with the right governance, the right orchestration, and the right human-on-the-loop controls will build a structural advantage. According to IBM’s 2025 Cost of a Data Breach Report, organizations not using AI and automation average $5.52 million per breach. Those using it extensively average $3.62 million. 

That gap widens every year. The only way to close it is to move faster than the threat. Modern security teams are doing exactly that with Torq’s AI SOC — autonomously, securely, and at machine speed.

Ready to see what autonomous security operations actually look like? Start with the AI or Die Manifesto.

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