January 14, 2026 5 Minute Read

Every now and then, LevelBlue SpiderLabs diverts a bit from its normal course of discussing vulnerabilities, ransomware attacks, and malware, and generates a public service blog to help those in the cybersecurity industry improve their skillset or better understand how the world is changing.

We did this a few years ago with this blog, and since artificial intelligence is now sucking all the oxygen out of the room, it’s time to see how AI is impacting cybersecurity careers and what you need to do to stay up to date.

The AI revolution is here. Organizations are rapidly integrating AI into their security operations, creating demand for professionals who understand both domains. Whether you're a security veteran looking to pivot or a newcomer choosing your path, the opportunity window is open but narrowing.

Why This Matters:

The professional world is bifurcating into the AI-capable and AI-obsolete. Security professionals who integrate AI aren't just adding a skill; they're multiplying their impact. One person who can build, deploy, and secure AI systems becomes more valuable than five who can't.

Here are some points to help position yourself for AI security roles.

Technical Skills Foundation

Build, Build, Build!!!

Learning the tools that underpin AI is everything.

I personally recommend Claude Code, but in the same way AI is changing how we operate practically on a daily basis, my suggestion could change next week. For those not familiar with Claude Code, it’s an anthropic agentic coding tool that lives in your terminal and helps you turn ideas into code faster than ever before.

With that first lesson under our belt, let’s set an objective that starting today and every day forward, your goal is to use AI tools for everything possible.

Programming Languages

The advent of AI means you no longer need to be a savant-level programmer, but must have a good understanding of programming languages, and any enterprise-level software design will gain you extra points.

There are a lot of coding languages out there, this is not news to the experienced, but this is a good list to understand what they are and how they are used, e.g.:

• Python: The lingua franca of AI/ML. Learn data manipulation (pandas, numpy), ML libraries (scikit-learn), and deep learning frameworks (TensorFlow, PyTorch)
• R: Valuable for statistical analysis and security data visualization
• SQL: Essential for querying security datasets and log analysis at scale

ML/AI Frameworks

Again, there is no need to be an expert, but it’s a plus to have some understanding where some models are used:

• TensorFlow/PyTorch: Build and train models for threat detection Hugging Face Transformers: Apply large language models to security use cases AutoML tools (H2O.ai, AutoKeras): Rapid prototyping for security applications

Security-Specific AI Tools

The list of security AI tools is expanding exponentially. Here are a few:

• Adversarial ML platforms (CleverHans, Foolbox): Understand AI system vulnerabilities
• SIEM with AI capabilities (Splunk ML Toolkit, Elastic ML): Leverage AI in production environments
• MLSecOps tools: Secure the AI pipeline itself

Certifications & Credentials

Build credibility with recognized certifications:

AI/ML Certifications:

• AWS Certified Machine Learning - Specialty: Cloud-scale ML deployment
• Google Professional ML Engineer: End-to-end ML engineering
• Microsoft Azure AI Engineer Associate: AI solution development

AI Security Specializations:

• CERT AI Security Professional (CAISP): AI-specific security practices
• GIAC Security Leadership (GSLC) with AI focus: Strategic AI security management
• ISC2 CC or CISSP + AI coursework: Traditional security foundation enhanced with AI

Cloud Platform Certifications:

• Azure: AZ-104 (Administrator), AZ-500 (Security Engineer), SC-100 (Cybersecurity Architect)
• AWS: AWS Certified Security - Specialty, AWS Certified Solutions Architect - Associate

Practical Portfolio:

• Contribute to open-source AI security projects on GitHub
• Create a public repository showcasing AI security use cases
• Document real-world scenarios: adversarial attack detection, malware classification, phishing detection
• Write blog posts to show your interest in specific topics and to give back to the community.

AI + Security Integration Use Cases

Understand where AI solves real security problems:

Threat Detection & Response

• Anomaly detection: ML models identify unusual network behavior, user activity, or system access patterns
• Malware classification: Train models on malware signatures for faster identification
• Automated incident triage: NLP models categorize and prioritize security alerts

Vulnerability Analysis

• Code analysis: AI tools scan codebases for security vulnerabilities faster than traditional static analysis
• Patch prioritization: ML predicts which vulnerabilities are most likely to be exploited
• Attack surface mapping: AI identifies exposed assets and potential entry points

Compliance & Governance

• Data classification: Automated identification of sensitive data (PII, PHI, PCI)
• Policy enforcement: AI monitors compliance with security policies in real-time
• Audit automation: ML streamlines compliance reporting and evidence collection

Central Coordinator and Administrator for all Security Tools

Claude Code: Running Claude code on all of your security platforms provides a powerful tool for interfacing, diagnostics, and collaboration.

Career Strategy & Positioning

Position yourself effectively in the market:

Resume & LinkedIn:

• Lead with AI security projects, not just traditional security work.Quantify impact: "Implemented ML-based threat detection, reducing false positives by 40%"
• Highlight certifications and portfolio projects prominently

Networking:

• Join AI security communities (BSides AI, DEF CON AI Village, OWASP ML Security)
• Engage with researchers on Twitter/LinkedIn discussing adversarial ML
• Attend conferences: RSA, Black Hat AI security tracks, NeurIPS security workshops

Job Search Tactics:

• Target roles: AI Security Engineer, ML Security Researcher, MLSecOps Engineer, AI Red Team
• Look at fintech, healthcare, defense contractors—sectors with mature security + emerging AI needs
• Apply to AI startups needing security expertise to build trust with enterprise customers

How to Fight Your Way Into a Career

The AI job market is competitive, but understanding these realities gives you an edge:

AI Is the Future—Position Accordingly

Organizations are embedding AI into every function: security, operations, customer service, and development. This means that professionals in any sector that lack AI skills risk becoming obsolete in roles that could be automated.

Economic Efficiency Is King

Once you have polished your AI skillset, it’s time to show how it can create corporate value.The most obvious way is the fact that one AI-skilled professional can accomplish the same amount of work that previously required entire teams.

Aggressive Skill Acquisition

The ROI I just mentioned might not always come from your firm. Don't wait for your employer to train you; ironically, they're often too slow. Invest your own time: nights, weekends, personal projects. This can include building on public entities like GitHub repos, creating and posting blogs, and attending and participating in AI-related conferences. Additionally, a high level of urgency is needed, as every month without AI skills is a month in which competitors gain ground.

Industry Outlook & Emerging Roles

High-Growth Role Categories:

• AI Red Team Specialist: Stress-test AI systems, perform adversarial attacks, identify model weaknesses
• MLSecOps Engineer: Secure the ML pipeline—data poisoning prevention, model integrity, deployment security
• AI Compliance Officer: Ensure AI systems meet regulatory requirements (EU AI Act, NIST AI RMF)

Market Trends:

• Regulatory pressure: New AI regulations (EU AI Act, executive orders) create compliance roles
• AI supply chain security: Organizations need experts to secure third-party AI vendors and models
• Responsible AI: Ethics and bias detection roles are emerging, requiring a security mindset

Salary Expectations:

• Entry-level AI security roles: $90K-$130K
• Mid-level (3-5 years): $130K-$180K
• Senior/specialized: $180K-$250K+

Remote Opportunities:

AI security is remote-friendly, with major tech companies, consultancies, and startups hiring globally.

Summary

Key Takeaways:

• Build foundation: Python, ML frameworks, and security-specific AI tools.
• Get certified: Combine traditional security certs with AI/ML credentials.
• Understand use cases: Focus on threat detection, vulnerability analysis, and compliance—where AI delivers measurable security value.
• Position strategically: Build a portfolio, network in AI security communities, target emerging roles.
• Act now: The market is expanding rapidly; early movers gain a competitive advantage.

Next Steps:

• Enroll in one AI/ML certification program this quarter.
• Build one AI security project for your portfolio.
• Join an AI security community and attend one event.

References

Certifications:

AWS ML Specialty

Google ML Engineer

Learning Resources

AI Village (DEF CON)

OWASP ML Security

MITRE ATLAS (Adversarial ML)

Frameworks & Tools

NIST AI Risk Management Framework

Microsoft Responsible AI Resources

Industry Reports

Cybersecurity Ventures AI Security Report

Gartner Market Guide for AI Security

(ISC)² Cybersecurity Workforce Study