AI Security Solutions: 2025 Guide & Controls

Protect your AI systems with proven security solutions and controls. This guide covers frameworks, threats, and implementation strategies for 2025.

What Is AI Security and Why It Matters

Artificial intelligence security protects four domains: the model, the data that trains it, the pipeline that builds and deploys it, and the infrastructure where it runs.

Each domain faces distinct attack types. Ransomware attacks on AI workloads can encrypt training datasets worth millions. Prompt injection attacks turn helpful chatbots into data theft tools. Data poisoning corrupts model accuracy while embedding backdoors attackers can exploit months later.

Machine learning-enabled attacks scale faster than human defenders can respond. The gap between security tactics and protection is widening because machine learning security differs fundamentally from traditional cybersecurity. AI systems evolve continuously, ingest unvetted data, and expose new attack surfaces like prompt injection, data poisoning, and model theft. Defending against AI security vulnerabilities demands techniques that go beyond patching servers or scanning binaries: adversarial testing, model provenance tracking, and input sanitization.

Regulators have noticed. Draft standards like ISO/IEC 42001 set management-system requirements for AI security, pushing you to prove governance across the entire lifecycle. Lawmakers worldwide are crafting statutes that will drive disclosure duties and fines for unsafe deployments. Building compliant defenses starts with understanding what you're defending against.

Critical AI Security Threats in 2025

Attack surfaces have shifted from traditional endpoints to data pipelines, model APIs, and specialized hardware. Security teams tracking AI security risks need to understand six critical threat categories.

1. Adversarial examples represent pixel-level or token-level tweaks that fool vision models, voice systems, or large language models into misclassification. A single imperceptible change can derail decision-making. The technique exploits mathematical vulnerabilities in how models process inputs, allowing attackers to weaponize tiny modifications that human eyes cannot detect.
2. Data poisoning attacks seed malicious or biased samples into training datasets, degrading model accuracy while embedding secret backdoors. The compromise happens during development and propagates through every iteration until discovered. By targeting the foundation of machine learning, poisoning attacks create systemic vulnerabilities that traditional security scanning often misses.
3. Prompt injection and jailbreaks override a large language model's system prompts with carefully crafted inputs, forcing it to reveal proprietary data or generate disallowed content. Public Bing Chat jailbreaks demonstrated how conversational systems can be manipulated, turning helpful assistants into data extraction tools. These AI security vulnerabilities have evolved from academic curiosities to mainstream threats.
4. Model theft and cloning attacks exploit interactive AI interfaces by repeatedly querying endpoints to reconstruct weights or decision boundaries. Adversaries effectively steal intellectual property and lower the barrier for future attacks, threatening the competitive advantage of organizations that invested heavily in proprietary model development.
5. Supply-chain compromise introduces vulnerabilities through third-party data sources, open-source libraries, or pre-trained models. Once embedded in the development pipeline, these vulnerabilities propagate to every downstream workload, creating systemic risks that traditional security scanning often misses.
6. Infrastructure overload and resource hijacking floods large language models with compute-intensive prompts to trigger denial-of-service conditions or inflate cloud bills. More sophisticated variants compromise GPU resources, conscripting them into botnets for distributed attacks.

Defending against these six threats requires both technological solutions and enforceable policies.

AI Security Solutions and Controls Defined

AI security solutions are technologies you deploy to protect machine learning systems. Solutions provide the technical capabilities to detect and stop threats in real time.

AI security controls are enforceable policies and procedures you implement across the AI lifecycle. Controls establish the governance framework that ensures consistent security practices.

Both work together to protect AI systems. Here are common examples:

Solutions (Technologies You Deploy)	Controls (Policies You Enforce)
AI security software like adversarial-testing libraries	Zero-trust access to training and inference endpoints
Runtime LLM firewalls that sanitize prompts and responses	SBOMs for models plus an AIBOM to document every dataset, weight, and dependency
XDR platforms enriched with AI telemetry	Policy-as-code gates in CI/CD pipelines that block unsigned models

Your organizational maturity, infrastructure location, and regulatory obligations determine which approach to prioritize. A start-up shipping models from a managed cloud may lean on provider-supplied AI security software. A healthcare enterprise with on-premises GPUs and HIPAA oversight needs granular access controls, immutable audit trails, and airtight SBOMs.

Maturity matters because technical fixes without governance rarely stick. Rolling out an adversarial test suite without threat-modeling discipline exposes issues faster than your team can triage them.

Solutions amplify controls. You harden the model with a library and enforce its use with a pipeline gate. You monitor drift with XDR and obligate response through an incident-handling SOP. Layering technologies with disciplined governance achieves defense-in-depth that stands up to hybrid, fast-moving threats.

Established AI Security Frameworks

Implementing AI security solutions and controls requires a structured approach. Frameworks provide the blueprints that help security teams deploy technical defenses while maintaining governance requirements.

Five frameworks tackle different risk layers and work together when deployed as a unified approach.

1. Google's Secure AI Framework (SAIF) provides a six-pillar engineering playbook spanning development, deployment, execution, and monitoring. It addresses critical issues with concrete safeguards: prompt-injection filtering, provenance checks for third-party models, and watermarking techniques that find model theft.
2. The NIST AI Risk Management Framework defines risk management through categorization, control selection, implementation, assessment, authorization, and continuous monitoring. It guides you to inventory assets, quantify risk, and tie mitigation actions to measurable outcomes. Organizations often map SAIF's technical requirements to NIST's assessment and monitoring steps for audit compliance.
3. ISO/IEC 42001 formalizes continuous improvement policies and processes for AI systems, including mandatory leadership commitment, document control, and periodic internal audits. Its broader focus on AI governance extends beyond model, data, and supply-chain security.
4. MITRE ATLAS takes a practitioner's approach to real adversary tactics. Its attack technique matrix covers data poisoning through resource hijacking, enabling threat modeling with the same rigor used for traditional infrastructure.
5. The OWASP LLM Top 10 targets language-model vulnerabilities like prompt injection, excessive agency, and training-data leakage. Combining these findings with SAIF input-sanitization controls delivers quick wins for API-exposed LLMs.

Together, these frameworks create an audit-ready security stack. SAIF handles daily engineering, OWASP addresses LLM specifics, MITRE provides threat intelligence, NIST manages risk oversight, and ISO/IEC 42001 ensures corporate compliance.

AI Security Solutions Across the Development Lifecycle

Protecting AI systems requires security integration at every lifecycle stage, from initial development through daily inference operations. Threats emerge at different moments, so defenses must match this timing.

• Development stage security begins with clean inputs. Enforce strict dataset provenance checks, then run automated security linting on prompts and training code. Before training begins, sanitize data to find poisoning or hidden backdoors and store signed hashes for later comparison. Red-team toolkits aligned with the MITRE ATLAS knowledge base stress-test models with adversarial inputs before public deployment.
• Build and CI pipeline security shifts left as models head toward production. Gate every merge with policy-as-code using OPA/Rego rules, and require cryptographic signing of model artifacts to prove lineage. A software bill of materials for every dependency, including pretrained weights, limits supply-chain surprises.
• Runtime monitoring serves as your safety net. Advanced platforms like the SentinelOne Singularity Platform provide autonomous threat detection and response capabilities that adapt to emerging AI-specific attack vectors. The platform uses AI for security operations, enabling real-time anomaly detection, telemetry collection, and explain ability dashboards that spot drift, unauthorized API scraping, or resource-exhaustion attacks immediately.
• Data-centric controls protect the foundation your models depend on. SAIF prescribes differential privacy, homomorphic encryption, and federated learning to neutralize membership-inference and model-inversion threats. Dataset provenance tracking provides protection against stealthy poisoning campaigns.

These solutions work together, not in isolation. Signed artifacts from CI pipelines feed trust signals into runtime verifiers, while monitoring alerts trigger automated retraining within secured development sandboxes.

Implement Controls in Your Pipeline

Security controls only work when integrated into existing workflows, not added as an afterthought. Frameworks like the NIST Risk Management Framework and the draft ISO/IEC 42001 agree on one principle: you secure a system by weaving controls through every step of its lifecycle. Here's a practical, stage-by-stage approach you can integrate into your existing MLOps workflow.

• Development begins with clean inputs. Enforce strict dataset provenance checks, then run automated security linting on prompts and training code. Before training begins, sanitize data to find poisoning or hidden backdoors and store signed hashes for later comparison. Adversarial test suites catch evasion tactics early, while unit tests for model outputs keep business logic honest.
• Build security gates every merge with policy-as-code using OPA/Rego rules and requires cryptographic signing of model artifacts to prove lineage. Continuous integrity scans align with the "Map" and "Measure" functions of the NIST framework.
• Deployment treats serving infrastructure like a high-value target. Isolate GPUs or accelerators in their own namespaces, rotate secrets that grant inference or fine-tuning rights, and restrict access with least-privilege service accounts. Input validation guards endpoints against prompt injection and adversarial payloads.
• Monitoring provides constant surveillance once live. Stream logs for every request and refusal, track drift thresholds, and flag anomalous resource spikes that might signal denial-of-service or cryptomining abuse. Platforms with Purple AI capabilities provide natural language security analysis and automated threat hunting across AI environments.

When something slips through, a tight incident loop keeps damage contained:

1. Detect: Real-time anomaly or drift trigger
2. Contain: Freeze affected endpoints, revoke exposed keys
3. Rollback: Redeploy last-known-good model and dataset hashes
4. Post-mortem: Update threat models, patch gaps, and document findings

Emerging Developments in AI Security Solutions

Dedicated GenAI firewalls are emerging as large language models integrate into daily operations. This specialized AI security software filters prompts and outputs for jailbreak attempts, sensitive data leaks, and policy violations at API endpoints. Security teams are increasingly using AI for security to detect threats that traditional tools miss.

Supply chain security has become a critical focus. Platforms are now testing continuous risk-scoring services that monitor third-party datasets, model weights, and plugins in real time.

Organizational structures are adapting. Security engineers, model-risk owners, and ethicists are merging into unified "AI assurance" teams. These groups balance robustness, privacy, and societal impact under one roof.

Offensive capabilities are advancing. Autonomous red-team agents are moving from research environments into production SOCs. These systems generate adversarial inputs continuously, probing models for vulnerabilities and accelerating mature AI security testing practices.

Regulation drives much of this evolution. The EU AI Act is expected to enter enforcement in 2026 after final approval and a transition period, with U.S. regulatory mandates still in the proposal stage. Organizations must prepare to document controls, incident response procedures, and model lineage or face significant penalties.

Deploy AI Security Solutions with SentinelOne

AI systems require security built into every lifecycle stage, not bolted on afterward. Start by mapping your current AI assets, including models, data pipelines, and inference endpoints, to understand your attack surface and identify AI security risks. Then implement the frameworks that match your maturity level: NIST for governance, SAIF for technical controls, and OWASP for LLM-specific threats.

SentinelOne's Singularity™ Platform uses behavioral AI to detect adversarial attacks, data poisoning, and prompt injection threats while enforcing governance controls across your development pipeline. Rather than generating thousands of alerts, the platform executes security operations autonomously, stopping threats in real time.

You can think like an attacker with SentinelOne's Offensive Security Engine™ and safely simulate attacks on your cloud infrastructure to better identify truly exploitable alerts. SentinelOne can identify more than 750 types of secrets hard-coded across your code repositories. You can prevent them from leaking out and gain full visibility in your environment. 

You can also eliminate false positives, save your team thousands of hours on validating findings and get proof of exploitability with Verified Exploit Paths™. You can stay on top of the latest exploits and CVEs. Singularity™ Cloud Security is an AI-powered, CNAPP solution that can stop runtime threats. Its AI Security Posture Management module can discover AI pipelines and modules. You can configure checks on AI services and leverage verified exploit paths for AI services as well.

Prompt Security for homegrown applications lets 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 for AI 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. Prompt for home grown apps 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 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 usage and blocks it to prevent outages and protects against denial of wallet attacks.

Security teams need platforms that understand both traditional threats and AI-specific attack vectors. Schedule a demo with SentinelOne to see autonomous threat detection in action.

AI Security Solutions FAQs

How do AI Security Threats differ from Traditional Cybersecurity Risks?

AI security threats target the mathematical foundations of machine learning models rather than code vulnerabilities. Adversarial examples, data poisoning, and prompt injection attacks exploit how models process and learn from data, requiring specialized defenses beyond traditional security controls. Traditional tools scan for known signatures, while AI security demands continuous monitoring of model behavior, input validation, and pipeline integrity throughout the development lifecycle.

What Frameworks should CISOs prioritize for AI Security Governance?

Start with the NIST AI Risk Management Framework for governance oversight, then implement Google's SAIF for technical controls. Add MITRE ATLAS for threat modeling and OWASP LLM Top 10 for language model-specific risks. This combination provides comprehensive coverage from strategy to implementation. 

Organizations typically map SAIF's technical requirements to NIST's assessment steps for audit compliance while using MITRE for red-team exercises.

How can Organizations measure AI Security Program Effectiveness?

Track key metrics including mean time to detect (MTTD) for AI-specific threats, percentage of models covered by security controls, adversarial test coverage, and incident response times. Monitor model drift detection accuracy and security control automation rates to measure program maturity. 

Establish baselines for false positive rates from automated testing and track the percentage of models with complete SBOMs and documented lineage.

What are the Biggest Compliance Challenges for AI Security?

Documentation and auditability pose the greatest challenges. Organizations must prove model lineage, demonstrate ongoing security testing, and maintain detailed records of AI governance decisions. Manual documentation processes don't scale as model deployment accelerates. 

Implementing policy-as-code and automated documentation helps address these requirements while reducing the burden on security teams during audits.

How do Autonomous Security Platforms improve AI Protection?

Autonomous security platforms use AI for security operations, analyzing system behavior patterns to find anomalies and respond to threats faster than human operators. They can identify subtle data poisoning attempts, unusual API usage patterns, and model drift indicators that traditional security tools miss. 

These platforms adapt to new attack techniques through behavioral analysis rather than relying on signature databases, providing comprehensive protection across the AI lifecycle.

What's the Difference Between AI Security Solutions and AI Security Controls?

AI security solutions are technologies you deploy, such as adversarial testing libraries, runtime LLM firewalls, and XDR platforms with AI telemetry. AI security controls are enforceable policies and procedures you implement, such as zero-trust access to endpoints, SBOMs for models, and policy-as-code gates in CI/CD pipelines. 

Effective AI security programs layer technological solutions with disciplined governance controls to achieve defense-in-depth that addresses both technical vulnerabilities and organizational risk.