Cybersecurity has a math problem: there are too many signals, too many vulnerabilities, and too few people. SOC teams drown in alerts. Threat intel feeds pour in raw data faster than analysts can read. Even basic tasks — like turning a vulnerability notice into a ticket with impact, assets, and recommended remediation — are repetitive.
Generative AI (GenAI) is attractive because it’s good at language and transformation. Cybersecurity work is, to a surprising extent, language work: writing playbooks, documenting incidents, creating user awareness content, translating threat intel into business terms, writing detection logic descriptions. If a model can read logs, correlate context, and then explain it in plain English, that’s a real productivity boost.
At the same time, attackers have the opposite challenge: their main friction points — writing convincing phishing emails, adapting malware to bypass basic defenses, creating believable personas — are also language and content problems. GenAI lowers that bar, too.
So we get a new cybersecurity landscape: AI-augmented defenders vs. AI-augmented attackers. The winners will be those who adopt faster, govern better, and treat AI as an operational capability — not a gadget.
2. High-Value Use Cases of Generative AI in Security
Let’s start with what’s working (or close to working) for defenders.
2.1 SOC Copilots and Analyst Assistants
Modern SOCs deal with thousands of alerts a day, and a large chunk are low-context: “Suspicious login from unusual location.” A generative model can:
- Ingest the alert
- Pull related context (user, device, recent logins, known threats)
- Summarize the probable scenario
- Propose next steps or even auto-generate a ticket
Instead of an analyst assembling the story, the AI drafts the story and the analyst edits it. That flips the time equation. Early adopters report significant time savings in triage and investigation because AI does the boilerplate drafting.
What it looks like in practice:
“Here’s the alert. Here’s the likely attacker path. Here are three similar incidents in the last 90 days. Here’s a recommended response and notification template.”
That’s not sci-fi — it’s an LLM sitting on top of log data and SOAR.
2.2 Threat Intelligence Summarization and Translation
Threat intel often arrives as long reports, different formats, and different languages. Analysts then need to answer: “Does this affect us?”
GenAI can:
- Summarize long TI reports into 1–2 business paragraphs
- Extract IOCs and map them to MITRE ATT&CK
- Cross-reference against known assets
- Localize content for different geographies
This is particularly valuable for multinational organizations — something firms like Capgemini often highlight when they talk about scalable, global security operations — because it reduces the lag from “global intel” to “local action.”
2.3 Policy, Procedure, and Playbook Drafting
Security teams spend painful amounts of time writing: incident response playbooks, data handling policies, third-party questionnaires, security awareness content. GenAI can generate first drafts that are:
- Aligned to a framework (e.g., ISO 27001, NIST CSF)
- Tuned to a role (developer vs. HR vs. executive)
- Written in simpler language for non-technical teams
The value here isn’t that AI writes the final policy — it’s that it gets teams to 60–70% faster, and humans finish it.
2.4 Developer Security (Shift-Left) Assistance
A lot of security risk comes from application code — insecure inputs, secrets in code, weak auth. Many organizations are already using AI coding assistants. Extend that to security and you get:
- AI explaining why a piece of code is vulnerable
- AI suggesting secure versions of functions
- AI converting security requirements into unit tests
- AI auto-generating SAST/DAST findings summaries for developers
This shrinks the gap between “security found something” and “developer understood and fixed it.”
2.5 User Awareness and Phishing Simulations
Training that is generic doesn’t work. Training that references your company context, your tools, your tone of voice does. GenAI can:
- Generate company-specific phishing simulations
- Create multiple difficulty levels
- Produce microlearning content based on recent attack patterns
Because it’s generative, you can refresh content monthly, not annually — which keeps users on their toes.
2.6 Natural-Language Querying of Security Data
Security data lives in SIEMs, EDR, data lakes. Most people can’t write KQL or SPL well enough to get insights. LLMs can let analysts say:
“Show me failed logins from external IPs to privileged accounts in the last 24 hours, grouped by source country.”
The AI turns that into the right query, runs it, and explains the result. That opens security data to more people.
3. The Dark Side: How Attackers Use Generative AI
Now the uncomfortable part: everything above has a mirror image.
3.1 Better, Cheaper, Faster Social Engineering
Bad phishing used to be obvious: bad grammar, weird tone, wrong context. LLMs fix that. Attackers can now:
- Generate emails in perfect local language
- Mimic corporate tone (by scraping your website/LinkedIn)
- Personalize at scale (role, department, ongoing project)
- Keep it short and urgent — the hardest to detect
In other words, GenAI collapses the skill gap. You no longer need a great English writer to run a great phishing campaign.
3.2 Deepfakes and Voice Cloning for Business Compromise
We’re entering the “I just got a call from the CFO” era. With a few minutes of audio and a public video, attackers can produce:
- Voice clones to instruct payments
- Deepfake videos to “approve” urgent actions
- Synthetic identities to pass remote KYC checks
Combined with email compromise or spoofed domains, this becomes very convincing. The real danger is not perfect deepfakes — it’s plausible deepfakes delivered at the right time, into an already-primed business process.
3.3 Automated Recon and Target Profiling
Generative AI can ingest open-source data (websites, social media, GitHub, job postings) and generate attacker-friendly briefs:
- “Top external-facing apps of Company X”
- “Likely tech stack and cloud providers”
- “Employees mentioning VPN issues”
- “Suppliers with weak security”
This dramatically reduces the research time of an attacker. Think of it as AI-assisted OSINT.
3.4 Malware, Exploit, and Tooling Assistance
Most responsible AI platforms block straight-up “write me ransomware” requests, but determined actors can:
- Ask for code fragments
- Ask for obfuscation techniques
- Ask for packing strategies
- Ask for exploit explanations
- Chain smaller models locally without guardrails
Add open-source offensive security frameworks to the mix, and GenAI becomes the “tutor” for junior attackers. The bar to entry drops.
3.5 Adversarial Use of AI Against AI
As defenders deploy LLMs, attackers will start to:
- Prompt-inject to exfiltrate data from the model
- Feed poisoned/ambiguous inputs to cause misclassification
- Try to map a model’s guardrails and work around them
- Flood AI-enabled SOCs with “noise” that looks real
So AI doesn’t just create new attack vectors — it becomes an attack surface itself.
4. Why This Is Different from Earlier Security Tech Waves
Security has seen hype cycles before: SIEM, UEBA, SOAR, XDR. Generative AI is different in a few ways:
- It’s general-purpose. LLMs aren’t “for security” — they’re for text, reasoning, structure. That means they’ll show up in HR, finance, procurement. Security has to protect AI it didn’t deploy.
- It’s easy to adopt at the edge. A single employee can start using a public GenAI tool to handle customer data. Shadow AI will be bigger than shadow IT.
- It’s dual-use by design. A better phishing detector and a better phishing writer can be the same underlying technology.
- It accelerates humans. This is the good part. It’s the first wave that visibly reduces toil for analysts — which is why many enterprises are pushing it forward despite the risks.
5. What Businesses Must Do to Adapt
Here’s the part most executives want: what’s the playbook? We can group it into six moves.
5.1 Establish AI Security and Governance Early
Treat GenAI like you treated cloud 10 years ago — with structure.
- Create an AI usage policy: what data can/can’t go into public models; approved tools; logging requirements.
- Classify AI interactions as data flows: if a model can see customer PII, that’s a regulated flow; log it.
- Define model ownership: security needs to know who runs which model (IT, data, a vendor?).
This avoids the “we discovered 12 shadow GPTs in finance” situation.
5.2 Secure the AI Supply Chain
If you’re using third-party or open-source models, or if you’re fine-tuning, you need to think about:
- Model provenance (where did it come from?)
- Model integrity (is the artifact tampered?)
- Training data trust (could it be poisoned?)
- Prompt and output logging (for forensics)
This is similar to software supply chain security (SBOM, signing, attestation), and the industry is already moving toward “Model BOMs” and AI-specific attestations. Start lightweight, but start.
5.3 Harden the Human Layer Against AI-Enhanced Fraud
Because social engineering gets better, your human defenses must get stricter, not just “more aware.”
- Introduce step-up verification for high-risk actions (payments, credential resets, vendor changes).
- Move from “looks real” to “cryptographically verified.” Email security, DMARC enforcement, and verified communication channels become more important.
- Train with AI-powered phishing simulations so users see realistic attacks, not 2015-level spam.
In other words: assume deepfakes and perfect phishing are in play and design processes that don’t trust appearances.
5.4 Augment the SOC — Don’t Replace It
Use GenAI to:
- Summarize alerts
- Draft incident reports
- Enrich IOCs
- Generate hunting hypotheses
…but keep humans:
- Making containment decisions
- Approving risky actions (EDR isolation, account disable)
- Communicating to executives
- Tuning detection to your business
Think of AI as a “junior analyst who works fast but sometimes makes things up.” You wouldn’t let that analyst act without oversight.
5.5 Monitor for AI-Powered Attacks Specifically
Add detections and metrics for:
- Unusual volume of password reset attempts with perfect-looking emails
- Inbound audio/video communications in unusual workflows
- Sudden spikes in MFA fatigue attacks coupled with convincing messages
- Access to internal AI endpoints from external IPs
- Prompt injection or prompt exfiltration attempts on internal LLM apps
If you don’t log your AI apps, you’ll have blind spots.
5.6 Upskill Security and Risk Teams
This is critical. Many risks from GenAI will look like “we misconfigured a tool we didn’t fully understand.” So train teams to:
- Write and test prompts securely
- Recognize prompt injection patterns
- Evaluate LLM output reliability
- Review AI apps for data leakage
- Talk to business units about safe adoption
This is where external partners and consulting firms (like Capgemini and others) are often brought in: not just to deploy AI, but to operationalize it across global security functions.
6. Principles to Keep You Out of Trouble
To make this practical, here are five principles you can put on a slide tomorrow.
- Default to private / enterprise models for sensitive data. Don’t let staff paste customer info into public models.
- Human-in-the-loop for high-impact actions. If AI says “block this CEO account,” a human must click OK.
- Log prompts and outputs. If you can’t reconstruct what the model saw and said, you can’t investigate.
- Trust but verify AI-generated intel. Cross-check IOCs or remediation steps before pushing to production.
- Assume attackers have the same AI you do. If it helps you write a great phishing simulation, it helps them write a great phishing email.
7. The Coming Regulatory and Compliance Angle
Regulators are catching up. Expect to see:
- Requirements to document AI use in critical processes
- Expectations around model transparency and testing
- Sector-specific guidance (finance, health, public sector)
- Stronger rules on biometric/deepfake misuse
If you build governance now — usage policies, logging, model inventory — you’ll be ready to slot in those requirements instead of rebuilding later.
8. Conclusion: AI Won’t Replace Security Teams — But AI-Using Teams Will Beat Non-AI Teams
Generative AI in cybersecurity is not a neat, one-directional story. It genuinely makes defenders faster, clearer, and more scalable. It also makes attackers more convincing, more automated, and more personalized. That’s why calling it “the new defense and the new threat” is accurate.
Here’s the mindset shift:
- Don’t wait for “perfect” AI — use it now for summarization, drafting, enrichment.
- Don’t trust AI blindly — build controls, logs, and human review.
- Don’t pretend attackers won’t use it — redesign processes to verify identity and intent, not appearance.
- Don’t isolate AI in IT — make security part of every AI rollout across the business.
Enterprises that do this will get the upside — reduced SOC toil, faster incident reports, smarter awareness training — without opening the door to silent data leakage or AI-powered fraud. Those that don’t will find themselves fighting AI-enabled adversaries with 2010-era defenses.
That’s not a fight you want.
