Artificial intelligence is no longer experimental; it’s everywhere. According to the Stanford AI Index 2025 Report, 78% of organizations now use AI in their operations, up from 55% the previous year. Yet, as adoption skyrockets, so do the risks.
As machine learning becomes integral to business operations, the stakes are high. Traditional security approaches no longer suffice to protect these new environments.
Enterprises now face a critical decision: adapt their security infrastructure to protect ML systems, or risk catastrophic breaches.
This isn’t just about securing another application; it is about securing entire decision-making systems.
In this article, we’ll explore a new class of ML-specific threats, why conventional cybersecurity falls short, and why monitoring and protecting ML systems matters now more than ever.
Machine Learning Faces Its Own Brand of Sophisticated Attacks
Traditional software has well-known weak spots.
Hackers use SQL injection to compromise and steal sensitive databases. They exploit cross-site scripting to hijack user sessions. Your security team knows how to implement proactive defense against these evasive attacks because they’ve been doing it for years.
Machine learning security is different. Attackers don’t just exploit code vulnerabilities. They manipulate how the machine learns and makes decisions.
When someone hacks a web application, you might lose customer data integrity or face downtime. When someone compromises an ML model, it makes wrong decisions systematically. The model itself becomes the weapon.
Think about that difference. A compromised web server affects that one system. A compromised ML model affects every decision it makes, potentially for months, until someone notices something is wrong.
So, how do you protect against threats that differ from traditional attacks?
Implementing FortiAI for enterprise protection is an efficient solution that addresses these unique ML threats with purpose-built cybersecurity capabilities.
Four Ways Attackers Target Machine Learning Systems
Method #1: Evasion Attacks
Attackers make tiny changes to input data that humans barely notice. However, these changes trick the model into making entirely incorrect predictions.
Your intrusion prevention system might classify malicious network traffic as normal. Your fraud detection might waive fraudulent transactions.
The changes are so subtle that your team won’t spot them by looking at the data.
Method #2: Data Poisoning
This attack happens during training. Someone corrupts the data your model learns from. They might upload new poisoned data or modify existing training data.
The model learns the wrong patterns. Worse yet, attackers can insert backdoors that activate under specific conditions.
You won’t catch this by testing the model’s normal behavior.
Method #3: Model Extraction
Competitors or hackers can reverse-engineer your proprietary ML models by feeding them inputs and studying the outputs. They essentially steal the intelligence you spent time and money developing.
We’ve seen major AI companies accuse each other of this recently. If it happens at that level, it’s definitely happening to enterprise ML systems.
Method #4: Supply Chain Vulnerabilities
Machine learning models depend on numerous external packages and libraries. Each dependency creates a potential entry point.
A compromised package in your ML pipeline can affect your entire system. Loading a shared machine learning model carries the same risk as running untrusted code on your network.
The Business Impact: Why This Matters Now
Let’s talk about what these threats actually cost your enterprise.
A. Financial and Operational Efficiency Risks
Companies using AI-driven security tools see faster threat detection and response. They stop breaches before they spread.
AI-driven cybersecurity helps organizations reduce breach costs by an average of over $2 million per incident compared to those without AI. However, these savings depend on securing the AI systems themselves, as ungoverned AI can introduce new vulnerabilities and risks.
In fact, IBM’s 2025 Cost of Data Breach Report found that 13% of organizations have already experienced breaches of AI models or applications, with 97% of those lacking proper AI access controls, resulting in 60% of incidents compromising sensitive data.
When ML security fails, the damage multiplies:
- Your recommendation engine makes bad suggestions
- Your predictive maintenance schedules unnecessary downtime
- Your automated trading system makes losing bets
- Your fraud detection misses obvious fraud
- Your customer service bots give wrong answers
Pick your use case; if the ML model is compromised, everything downstream suffers.
B. Compliance and Regulatory Pressure
With data-protection rules such as the General Data Protection Regulation (GDPR), NIST AI frameworks, and HIPAA, the pressure on enterprises to implement policy controls over how data privacy is processed is growing.
AI/ML systems magnify compliance challenges: they rapidly consume and transform sensitive data, often across cloud, edge, and hybrid environments. Organizations require automated compliance monitoring tailored to AI workflows, rather than relying solely on traditional rules.
C. Competitive Disadvantage
In 2025 and beyond, businesses that adopt AI-driven IT strategies will gain agility, resilience, and competitive advantage. Conversely, organizations without ML security face deployment delays, AI innovation bottlenecks, and weaker customer trust.
If your AI-powered services are compromised or mishandled, you lose more than data; you risk customer trust and your brand’s credibility.
The business case is clear: ML security is no longer a nice-to-have; it underpins competitiveness, compliance, and operational resilience.
Why Traditional Security Approaches Fall Short
Fundamental Mismatches
Security approaches built around firewalls, VPNs, perimeter defense, and vulnerability scanning do not align with the speed, architecture, and behavioral nature of ML systems.
Conventional tools struggle to map ML pipelines, monitor inference behavior, or detect model manipulation.
Meanwhile, ML engineers and data scientists work in specialized environments (e.g., Jupyter Notebooks, Databricks, cloud GPU clusters) not typically addressed by standard IT-centric layered security pipelines.
The MLSecOps Gap
While traditional DevSecOps focuses on securing application code and infrastructure, MLSecOps (or ML security operations) extends these principles to cover confidentiality, integrity, availability, and traceability of data, software, and models throughout the ML lifecycle.
A critical gap often arises because security teams lack machine-learning expertise and data science teams lack security operations maturity.This gap becomes a focal point for risk: models are deployed without a full understanding of how to monitor or protect them.
In short, traditional security and network operations leave blind spots in ML pipelines, and attackers know it.
What You Should Do Right Now
- Adopt Zero Trust for AI Systems
Stop assuming your ML systems are trustworthy by default.
Enforce least-privilege access. Verify every interaction with your AI systems continuously. This approach catches emerging threats from insiders and compromised credentials before they damage your models.
- Implement Continuous Monitoring
Set up real-time visibility into how your ML models behave. Automate compliance checks. Don’t wait for quarterly audits to discover problems.
- Establish AI Governance
Create clear policy updates for model deployment and usage. Form cross-functional teams that include data scientists, security professionals, and compliance staff. Everyone in the business needs to understand their specific role in keeping ML systems secure.
- Secure Your Supply Chain
Vet every ML framework and dependency before using it. Maintain an updated inventory of all ML assets. Know what you’re running and where it came from.
The Bottom Line
Machine learning security is no longer optional. It’s a basic requirement for doing business.
Organizations that proactively secure their AI infrastructure with solutions like FortiAI will spend the next decade innovating. Those who wait will spend it responding to breaches and falling behind competitors.
The question now isn’t whether to invest in ML security; rather, it is whether you can afford to wait any longer.
Your ML systems are already running. The emerging threats targeting them are already active. Every day without proper ML security increases your risk.
Start treating machine learning security as seriously as you treat every other critical business system. Because in 2025, if your AI isn’t secure, your business isn’t safe.
