What Is AI Security?
Artificial intelligence (AI) security is the discipline focused on protecting data, models, code, and infrastructure from malicious attacks, unauthorized access, and accidental misuse.
Traditional cybersecurity focuses on fixing clear-cut software bugs, while AI security has to deal with models that can behave unpredictably and can be tricked just by messing with the data they learn from.
Even a single slightly altered input can make an AI give the wrong answer, and just a few poisoned training examples can secretly teach it bad behaviors that show up later.
This guide provides concrete AI security best practices by walking through the unique attack surface of machine learning (ML) and providing actionable AI security guidance.
The Evolution of AI Threats
AI systems open new attack surfaces across data, models, and prompts that traditional security tools cannot fully protect. Without specialized defenses, attackers can subvert, steal, or weaponize AI before safeguards catch up.
Traditional attacks like phishing, ransomware, or SQL injection were more predictable. They targeted networks and code, and defenders could respond by patching software, closing vulnerabilities, and hardening infrastructure.
AI has changed that. Instead of exploiting code, attackers now exploit data and logic driving machine intelligence. Because AI systems learn and adapt, they can be manipulated in ways traditional security tools were never built to detect. A few poisoned samples slipped into a training set can quietly distort a model’s decisions, letting malicious emails bypass a spam filter while appearing completely normal to human reviewers.
Generative AI models have opened an even wider door. Attackers can craft prompts that make large language models (LLMs) leak private data, generate prohibited content, or execute harmful code despite built-in safeguards. These techniques can scale into automated "jailbreak" chains that repeatedly bypass controls and mass-produce exploits.
Models hidden behind the scenes are also vulnerable. Systematic querying lets adversaries clone proprietary models, undermining years of R&D investment. Subtle adversarial examples include imperceptible image tweaks or byte-level malware variants that confuse classifiers and evade defenses. Hidden backdoors implanted during training can stay dormant for months, only activating when a secret trigger appears.
These kinds of threats are still emerging, and many are seen in research or controlled environments. But they highlight gaps that traditional security measures were not designed to cover. Addressing them requires safeguards that protect data, models, and supporting infrastructure as AI becomes more widely deployed.
Why AI Systems Need Specialized Security Controls
Securing traditional applications relies on hardening code and patching servers. Machine learning (ML) systems face a different challenge where two distinct vulnerability classes converge in every ML workflow:
- Data-centric threats like poisoning and biased labeling compromise what the model learns
- Code-centric threats exploit how the model runs through backdoors in third-party dependencies.
In other words, AI systems need specialized security controls because the entire ML lifecycle has become an attack surface.
Poisoned samples slip in during data collection. Malicious libraries execute arbitrary code in development. Adversarial inputs target inference APIs post-deployment. Unnoticed drift silently erodes performance in production. Dozens of entry points across every stage exist, highlighting the complexity of AI and machine learning security.
Traditional controls miss these tactics entirely. Next-gen firewalls can’t detect imperceptible pixel changes that flip classifiers. Conventional CI/CD scans won’t flag poisoned datasets.
Implementing AI security best practices requires dataset provenance checks, adversarial testing, and tamper-evident storage. These create immutable records that trace every model, parameter, and data point back to its source.
Another reason AI systems need specialized security controls is regulation and compliance. The EU AI Act mandates continuous monitoring and cybersecurity by design for "high-risk" systems, with fines reaching 7% of global turnover for non-compliance. GDPR adds data minimization requirements, while U.S. executive orders focus on broader AI risk management and cybersecurity principles.
Meeting these expectations and staying ahead of adversaries demands security best practices, not recycled controls.
12 Essential AI Security Best Practices
These AI security best practices provide comprehensive protection across the entire ML lifecycle. Each practice addresses specific vulnerabilities while building toward a unified defense strategy.
1. Implement Data Governance Frameworks
AI security begins with a robust data governance framework. By managing data quality, access, and compliance, organizations ensure that their systems are built on reliable foundations.
Start by defining a governance policy that encompasses data integrity, classification, and retention standards, encouraging stakeholder collaboration to ensure policy adherence while evolving these standards to address emerging challenges. A common pitfall is the failure to regularly update governance policies, leading to outdated practices in the rapidly changing data environment. Measure effectiveness by tracking reductions in data breaches or governance infractions.
2. Secure Training Data Pipelines
Securing the data supply chain is crucial to mitigating risks such as data poisoning. Organizations should employ encryption protocols, ensure integrity validation, and set access controls throughout data pipelines. Encryption safeguards data in transit and at rest, while integrity validation ensures that data remains unaltered from input to output. Remember to track the lineage of data to identify potential sources of corruption. The all-too-common mistake of neglecting provenance tracking can leave systems vulnerable to undetected threats. Success can be gauged by documenting a decrease in unauthorized data access incidents.
3. Preserve Privacy
Techniques such as differential privacy and federated learning ensure data privacy without compromising model utility. Differential privacy introduces noise to data queries, preserving individual privacy while allowing aggregate insights. Federated learning keeps data decentralized, training models across multiple devices to enhance privacy. Integration into workflows can involve balancing between data utility and privacy, which remains a significant hurdle. Successful implementation is signaled by achieving regulatory compliance without significant dips in model performance.
4. Establish Model Versioning and Provenance
Tracking the lineage and changes of models through versioning and provenance is vital for model integrity. This involves creating an immutable registry that logs each model's creation, training parameters, and deployment details. Organizations often make the mistake of incomplete documentation, obscuring the evolution of models. By ensuring thorough and clear documentation, you can achieve complete traceability, which is vital for audits and identifying potential sources of model errors.
5. Deploy Adversarial Testing and Red Teaming
Ethical hacking reveals vulnerabilities within models before malicious actors exploit them. Red-teaming involves rigorous testing using known adversarial techniques, ensuring that models are fortified against potential attacks. Establish regular intervals for this testing, and stay aware of both known and emerging threats. Testing exclusively for known attack vectors can leave systems vulnerable to novel threats. Enhancing model robustness against adversarial examples is a key success metric.
6. Implement Model Access Controls
Protecting models from unauthorized use requires robust access controls. This means implementing strict authentication and authorization protocols in addition to continuous monitoring of model endpoints. Overlooking API security is a frequent oversight that leaves models exposed to unauthorized queries. Achieving zero unauthorized model access incidents indicates strong security and proper implementation of these controls.
7. Secure AI/ML development Environments
Security must extend to the entire development pipeline. Utilizing signed containers, isolated builds, and secure coding practices mitigates the risk of infiltration and tampering. Neglecting the security of development environments can introduce vulnerabilities at early stages, threatening the entire lifecycle. A reduction in pipeline security incidents reflects the efficacy of protective measures.
8. Implement Runtime Monitoring and Anomaly Detection
Consistent monitoring of systems in production is essential to capture real-time security anomalies. This entails deploying monitoring tools that flag strange behaviors and setting alerts for quick response. A common pitfall is focusing solely on performance metrics instead of security ones. Detecting anomalous behavior swiftly lowers the mean time to remediation.
9. Apply Zero-Trust Architecture to AI Systems
Adapting zero-trust principles to ML environments means implementing least privilege access and continuous verification protocols. The unexamined assumption that components are innately trustworthy can lead to significant risk. Success is measured by the shrinking of the attack surface, corresponding to stricter access and verification measures.
10. Create Security Policies and Standards
Developing organization-specific guidelines aligned with regulatory standards like NIST and ISO/IEC 42001 forms the backbone of effective AI governance. This includes establishing comprehensive policies, standards, and procedures. One frequent misstep is crafting policies that lack measurable actions or are impractical. Compliance rate across projects serves as a key metric for policy effectiveness.
11. Establish Focused Incident Response Plans
Tailoring incident response plans for specific threats allows better handling of unique vulnerabilities. Crafting playbooks that address common incidents is crucial for preparedness. Treating specialized incidents the same as other security incidents can hamper effective responses and indicate an adaptive strategy.
12. Continuous Compliance Monitoring and Auditing
Ensuring constant adherence to regulations requires implementing automated compliance checks and routine audits. Organizations must balance AI risk management requirements with operational efficiency while maintaining robust oversight. Sole reliance on point-in-time assessments, however, may leave gaps. The success metric is a high pass rate in compliance audits, indicating robust and ongoing adherence to necessary legal standards.
Building AI Security Resilience with SentinelOne
As AI systems reshape the digital landscape, their growing attack surface demands a security As AI systems reshape the digital landscape, their growing attack surface demands a security strategy built for the realities of ML. Traditional controls alone cannot protect the data pipelines, models, and infrastructure that now drive critical business decisions.
Implementing the best practices outlined in this guide creates a foundation for defending AI systems, but translating strategy into action requires security solutions that can keep pace with the speed and scale of modern threats.
This is where SentinelOne can help. The Singularity Platform brings autonomous protection built for AI workflows, closing the gaps that traditional tools leave behind.
Purple AI acts as a behavioral security analyst, continuously learning from your environment to detect anomalous activity across AI infrastructure, which is critical for best practices like real-time monitoring and adversarial testing.
The platform also does AI Security Posture Management (AI-SPM) by automatically discovering AI pipelines and models, checking their configurations, and surfacing Verified Exploit Paths™ that show real attack scenarios, not just theoretical vulnerabilities. By reducing alert volume by up to 88% in MITRE evaluations, SentinelOne helps teams focus on real AI security incidents rather than chasing noise. SentinelOne’s Prompt Security helps you protect against prompt injection, data leaks and harmful LLM responses. Prompt for employees can establish and enforce granular department and user rules and policies. It can coach your employees on the safe use of AI tools with non-intrusive explanations.
Prompt Security for code assistants can help you adopt AI-based code assistants like GitHub Copilot and Cursor while safeguarding secrets, scanning for vulnerabilities, and maintaining developer efficiency. SentinelOne's Prompt Security can surface shadow MCP servers and unsanctioned agent deployments that bypass traditional tools. You can get searchable logs of every interaction for better risk management.
Prompt Security also helps you protect your data everywhere and safeguard all your AI-powered applications. You can also protect against shadow IT attacks, monitor and identify them and eliminate blind spots. You can also block attempts to override model safeguards and reveal hidden prompts. Plus, it detects abnormal AI usage and blocks it to prevent outages and protects against denial of wallet and service attacks.
As adversaries continue to adapt, organizations that embed AI security into their operations will be best positioned to innovate safely. SentinelOne gives security teams the visibility, automation, and confidence to protect the intelligence powering their business without slowing it down.
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Traditional cybersecurity focuses on protecting deterministic systems with known vulnerabilities, while AI security must address the probabilistic nature of ML models. AI systems face unique threats like data poisoning, adversarial examples, and model extraction that don't exist in conventional software.
AI security best practices require specialized controls for the entire ML lifecycle, from training data to model deployment.
Organizations should review AI security policies quarterly and update them whenever new regulations emerge or significant changes occur in their AI infrastructure. The rapid evolution of AI threats and emerging compliance requirements like the EU AI Act demand more frequent policy updates than traditional cybersecurity frameworks. Continuous monitoring helps identify when policy updates become necessary.
Data poisoning, model theft, and adversarial attacks represent the highest-priority threats for most organizations. These attacks can compromise model integrity, steal intellectual property, and bypass security controls. Organizations should also prioritize securing their MLOps pipelines and implementing proper access controls around model APIs, as these represent common attack vectors with significant business impact.
Small teams should start with foundational controls like data governance, model versioning, and access controls before expanding to advanced techniques. Leveraging automated tools and cloud-native security services can help resource-constrained teams implement comprehensive AI security best practices without overwhelming their capacity.
Focus on high-impact, low-maintenance controls that provide broad protection across multiple threat vectors.
