Every security vendor is now an “AI security company.” Every conference presentation promises to reveal the revolutionary new threats posed by artificial intelligence. Every CISO is being asked when they’re implementing their AI security strategy. But here’s the inconvenient truth: AI security isn’t fundamentally different from regular security.
Strip away the marketing hype and buzzwords, and AI security comes down to the same fundamental principles we’ve been applying to complex software systems for decades: know your assets, control access, monitor for anomalies, and respond quickly to incidents. The technology stack may be different, but the security challenges are remarkably familiar.
After working with organizations to secure AI systems and cut through vendor marketing, we’ve learned what actually matters for AI security and what’s just repackaged traditional security with new terminology.
The AI Security Hype Machine
Revolutionary Threats That Aren’t
The marketing narrative: AI systems face completely new types of attacks that traditional security can’t address.
The reality: Most “AI-specific” attacks are variations of existing attack categories:
- Prompt injection is input validation and sanitization failures
- Model poisoning is supply chain and data integrity attacks
- Adversarial examples are edge case exploitation and input fuzzing
- Model extraction is intellectual property theft and reverse engineering
Example: A vendor demonstrates a “sophisticated AI prompt injection attack” that tricks a chatbot into revealing training data. Strip away the AI terminology, and this is a classic input validation vulnerability – the same type of flaw that’s been causing SQL injection and XSS attacks for twenty years.
New Security Categories or Old Problems?
Traditional security already covers most AI security concerns:
Data security:
- Training data protection (data classification and access control)
- Model data privacy (data loss prevention and encryption)
- Data lineage and provenance (audit trails and integrity monitoring)
Application security:
- AI API security (same principles as any API)
- Model serving infrastructure (standard web application security)
- Integration security (traditional service-to-service authentication and authorization)
Infrastructure security:
- AI training infrastructure (standard cloud and compute security)
- Model storage and deployment (traditional asset and configuration management)
- Network security for AI systems (same network segmentation and monitoring principles)
Where AI Security Actually Differs
New Attack Surfaces, Familiar Controls
AI systems do introduce some genuinely new attack surfaces, but they’re addressable with traditional security approaches:
Model training pipeline security:
- New risk: Compromised training data that affects model behavior
- Traditional control: Supply chain security, data integrity verification, and change management
Model inference security:
- New risk: Malicious inputs designed to manipulate model outputs
- Traditional control: Input validation, rate limiting, and anomaly detection
Model deployment security:
- New risk: Model substitution and backdoor insertion
- Traditional control: Code signing, integrity monitoring, and deployment pipeline security
Scale and Automation Challenges
AI systems amplify existing security challenges rather than creating entirely new ones:
Scale of data exposure:
- AI systems process vast amounts of data, amplifying the impact of data breaches
- Traditional controls: data classification, access control, and monitoring at scale
Automated decision-making risks:
- AI systems make decisions without human oversight, amplifying the impact of manipulation
- Traditional controls: audit trails, decision validation, and automated response procedures
Dependency complexity:
- AI systems have complex dependencies on libraries, models, and data sources
- Traditional controls: dependency scanning, supply chain security, and configuration management
Practical AI Security Implementation
Start with Traditional Security Fundamentals
Before implementing AI-specific controls, ensure basic security hygiene:
Asset inventory and management:
- Catalog all AI systems, models, and data sources
- Understand AI system dependencies and integration points
- Map data flows and access patterns for AI systems
- Document AI system architectures and security boundaries
Access control and authentication:
- Implement proper authentication for AI system access
- Apply least privilege principles to AI training and inference systems
- Control access to training data and model artifacts
- Manage service accounts and API keys for AI system integration
Network security and segmentation:
- Segment AI training and production environments
- Monitor network traffic to and from AI systems
- Implement proper firewall rules for AI service communications
- Apply standard network security controls to AI infrastructure
AI-Specific Security Enhancements
Layer AI-specific controls on top of solid security foundations:
Training data security:
- Data classification and handling procedures for training datasets
- Data lineage tracking and provenance verification
- Training data validation and integrity checking
- Access control and audit logging for data preparation and curation
Model security and integrity:
- Model versioning and artifact management
- Model signing and integrity verification
- Backup and recovery procedures for trained models
- Change management for model updates and deployments
Inference and deployment security:
- Input validation and sanitization for model inputs
- Rate limiting and abuse prevention for AI services
- Output filtering and validation for model responses
- Monitoring and alerting for unusual inference patterns
Monitoring and Detection
Adapt traditional monitoring approaches for AI systems:
Behavioral monitoring:
- Baseline normal AI system behavior and performance
- Detect anomalous model outputs and inference patterns
- Monitor for unusual data access patterns during training
- Alert on performance degradation that might indicate attacks
Security event correlation:
- Integrate AI system logs with existing SIEM platforms
- Correlate AI security events with broader security monitoring
- Develop use cases for AI-specific security scenarios
- Train analysts on AI system security event investigation
Common AI Security Mistakes
Over-Engineering AI-Specific Solutions
The mistake: Implementing complex AI-specific security solutions without addressing basic security hygiene.
Better approach: Start with traditional security controls and enhance them for AI-specific risks as needed.
Falling for Vendor AI Security Theater
The mistake: Buying expensive “AI security platforms” that repackage traditional security capabilities with AI branding.
Better approach: Evaluate AI security solutions based on actual capabilities rather than marketing claims.
Ignoring Business Context
The mistake: Treating all AI systems as equally critical without understanding business impact and risk.
Better approach: Apply risk-based approaches to AI security that consider business context and impact.
Separate AI Security Programs
The mistake: Creating standalone AI security programs disconnected from broader security operations.
Better approach: Integrate AI security into existing security programs and operations.
AI Security Vendor Evaluation
Questions That Cut Through Hype
When evaluating AI security vendors, ask pointed questions:
About capabilities:
- “What specific security problems does this solve that traditional security tools can’t?”
- “How does this integrate with our existing security tools and processes?”
- “What’s your detection accuracy for false positives in real-world environments?”
About implementation:
- “What traditional security controls must be in place for this to work effectively?”
- “How much specialized expertise do we need to operate this solution?”
- “What’s the operational overhead compared to traditional security approaches?”
Red Flags in AI Security Marketing
Avoid vendors who:
- Promise to solve all AI security problems with a single platform
- Use lots of AI buzzwords but can’t explain specific security improvements
- Can’t articulate how their solution integrates with traditional security controls
- Focus on theoretical attacks rather than practical, observed threats
Building Practical AI Security Programs
Integration with Existing Security Operations
Successful AI security programs extend existing capabilities rather than replacing them:
SIEM integration:
- AI system log collection and analysis within existing SIEM platforms
- AI security use cases and correlation rules
- Integration of AI security events with broader security monitoring
Incident response integration:
- AI-specific playbooks and procedures within existing incident response programs
- Cross-training for incident response teams on AI system investigation
- Integration of AI security incidents with broader incident management
Vulnerability management integration:
- AI system vulnerability scanning and assessment
- Integration of AI security vulnerabilities with existing vulnerability management programs
- Risk scoring that considers AI system business impact and exposure
Skills Development
AI security requires some new skills but builds on traditional security expertise:
Core security skills that apply:
- Network security and monitoring
- Application security testing and code review
- System administration and infrastructure security
- Incident response and forensic investigation
AI-specific knowledge to develop:
- Understanding of machine learning and AI system architectures
- Familiarity with AI development and deployment pipelines
- Knowledge of AI-specific attack techniques and vulnerabilities
- Understanding of AI regulatory and compliance requirements
Metrics and Measurement
Measure AI security effectiveness using traditional security metrics:
Security posture metrics:
- AI system vulnerability assessment results
- AI system compliance with security policies and standards
- AI system security control coverage and effectiveness
Operational metrics:
- AI security incident frequency and impact
- AI system security event detection and response times
- AI security tool performance and false positive rates
Business metrics:
- AI system availability and performance impact from security controls
- Cost of AI security programs compared to risk reduction
- Business value delivery from AI systems with appropriate security controls
The Future of AI Security
Maturing Beyond the Hype
AI security is following the typical technology security maturation curve:
- Initial hype and fear about revolutionary new threats
- Gradual realization that traditional security principles apply
- Development of practical, integrated approaches
- Evolution toward standard security practices with AI-specific enhancements
Integration with Traditional Security
The future of AI security is integration, not separation:
- AI security capabilities built into traditional security tools
- AI system monitoring integrated with existing security operations
- AI-specific controls as extensions of traditional security frameworks
- Unified security programs that address both traditional and AI systems
Regulatory and Compliance Considerations
Emerging AI Regulations
AI-specific regulations are emerging, but most requirements align with traditional security principles:
- Data protection and privacy requirements for AI training data
- Algorithmic accountability and audit requirements
- Security and safety requirements for AI systems in critical applications
- Transparency and explainability requirements for AI decision-making
Compliance Integration
AI compliance requirements fit within existing compliance frameworks:
- Data governance and protection within existing privacy programs
- AI system security within existing information security management systems
- AI risk management within existing enterprise risk management programs
- AI audit and assurance within existing audit and compliance programs
Getting Started with AI Security
Assessment and Planning
Before implementing AI-specific security controls:
- Inventory all AI systems and understand their business context and risk profile
- Assess current security control coverage for AI systems
- Identify gaps between current capabilities and AI system security requirements
- Prioritize AI security improvements based on risk and business impact
Implementation Strategy
Start with security fundamentals:
- Apply traditional security controls to AI systems and infrastructure
- Enhance monitoring and detection for AI-specific risks and attack patterns
- Integrate AI security into existing security operations and incident response
- Develop AI-specific expertise within existing security teams
Vendor Selection
Choose AI security solutions based on practical value, not marketing hype:
- Focus on solutions that enhance existing security capabilities
- Evaluate based on integration with existing security tools and processes
- Test solutions with real AI systems and actual use cases
- Assess vendor expertise and track record in traditional security domains
The Bottom Line
AI security isn’t a revolution – it’s an evolution of traditional security principles applied to new technology systems. The most effective AI security programs build on solid security fundamentals and enhance them with AI-specific capabilities where needed.
Don’t let vendor hype convince you to abandon proven security approaches. Instead, apply the same rigorous, risk-based thinking that’s served security professionals well for decades.
What’s Next?
Ready to cut through AI security hype and build practical protection for your AI systems? Start with a thorough inventory of your AI systems and an honest assessment of your current security controls.
If you need help developing AI security strategies that focus on practical risk reduction rather than marketing buzzwords, let’s talk. We help organizations build security programs that actually work – whether they’re protecting traditional systems or the latest AI applications.
The fundamentals of good security don’t change just because the technology gets more sophisticated.