In August 2025, Lenovo’s GPT-4- powered chatbot was compromised, exposing customer data and highlighting the rapid deployment of AI tools without adequate safeguards. Google also reported a mass data theft incident linked to the Salesloft AI agent, prompting emergency shutdowns. These incidents demonstrate that AI systems aren’t only vulnerable but also actively being targeted.
According to Cisco’s 2025 AI Security Report , 84% of enterprises using AI have experienced data leaks, and 75% cite governance as their top concern. AI is now embedded in fraud detection, diagnostics, and customer service—but most organizations still rely on outdated security models. With threats such as prompt injection, model theft, and shadow AI growing rapidly, the need for a structured AI security framework is no longer optional.
In this blog, we’ll break down what an AI security framework actually is, why your enterprise needs one, and which specific frameworks can protect your AI investments. We’ll cover five proven approaches and help you choose the right one for your organization.
What Is an AI Security Framework? An AI security framework is a structured set of rules, processes, and tools designed to protect AI systems from misuse, adversarial attacks, and compliance risks.
Unlike traditional cybersecurity frameworks, AI security frameworks account for the unique nature of machine learning models, which can drift over time, learn from biased data, and be manipulated in ways that standard software cannot.
Think of them as blueprints for AI protection. They help enterprises:
Identify AI-specific risks Define controls to prevent misuse Ensure compliance with laws like the EU AI Act and HIPAA Build trust with customers and regulators Why Enterprises Need Specialized AI Security Frameworks AI systems don’t follow fixed rules. They learn, adapt, and sometimes fail in unpredictable ways. That brings new risks:
Model drift — AI starts making wrong decisions over time Adversarial inputs — attackers feed data that tricks the model Data leakage — sensitive info gets exposed through outputs Shadow AI — unapproved tools used by teams without oversight Compliance pressure — laws now demand transparency and accountability
Traditional cybersecurity tools don’t cover these. That’s why enterprises need frameworks built for AI.
Types of AI Security Frameworks Different frameworks have been created to help companies protect their AI systems. Each one tackles different problems. Some cover the entire AI lifecycle, while others focus on specific threats, and some address advanced autonomous AI agents .
Here’s a breakdown of the most important AI security frameworks in 2025:
1. NIST AI Risk Management Framework (AI RMF) The NIST AI RMF is one of the most widely referenced AI governance and security frameworks.
Key features:
Lifecycle-based: Govern, Map, Measure, Manage Provides structured risk assessment for AI projects Emphasizes trustworthy AI principles (fairness, transparency, accountability)
Best for:
Highly regulated industries like healthcare, finance, and government Enterprises that need a compliance-focused approach Teams seeking a systematic way to measure and reduce AI risk 2. Microsoft AI Security Framework Microsoft has introduced its AI Security Framework to ensure the responsible and secure use of AI.
Key features:
Covers Security, Privacy, Fairness, Transparency, Accountability Includes best practices for safeguarding data and preventing misuse
Best for:
Enterprises already using Microsoft Azure Organizations prioritizing responsible AI and governance alongside security Teams looking for a practical, implementation-ready guide 3. MITRE ATLAS Framework The MITRE ATLAS (Adversarial Threat Landscape for AI Systems) framework focuses squarely on AI threats and attacker tactics.
Key features:
Catalogs real-world AI attack methods, including:
Supports red teaming and threat modeling for AI systems Helps defenders anticipate how adversaries target AI models
Best for:
Organizations deploying machine learning models in production Enterprises wanting to understand and simulate attacker behavior AI In Cybersecurity: Why It’s Essential for Digital Transformation Explore AI tools driving threat detection, proactive security, and efficiency in cybersecurity.
Learn More
4. Databricks AI Security Framework (DASF) The Databricks AI Security Framework (DASF) bridges the gap between business, data, and security teams.
Key features:
Lists 62 AI risks and 64 controls Platform-agnostic but inspired by NIST AI RMF and MITRE ATLAS Provides practical controls that map to enterprise needs
Best for:
Data-heavy enterprises running large-scale AI pipelines Businesses seeking a comprehensive, control-based framework Teams wanting actionable steps instead of just principles 5. MAESTRO Framework for Agentic AI As enterprises adopt autonomous AI systems, like agentic AI , traditional frameworks fall short. That’s where the MAESTRO Framework comes in.
Key features:
Built specifically for agentic and autonomous AI
Detects evolving risks such as:
Includes guidance for AI-driven SOCs, RPA systems, and OpenAI API-based agents
Best for:
Security teams handling autonomous AI deployments
How to Choose the Right Framework Use NIST if you need structured risk management Use Microsoft if you care about ethical and responsible AI Use MITRE ATLAS if you need deep threat modeling Use Databricks DASF for cross-team collaboration Use MAESTRO if you’re working with autonomous agents
Many enterprises mix and match based on their AI maturity, risk profile, and compliance needs.
Framework Focus Area Best For Unique Strength NIST AI RMF Governance & Lifecycle Regulated industries Structured, compliance-ready Microsoft AI Security Responsible AI principles Azure ecosystems Balance of ethics + security MITRE ATLAS Threat modeling Security teams Adversarial attack catalog Databricks DASF Risk + control mapping Data-driven enterprises 62 risks, 64 actionable controls MAESTRO Agentic AI risks Autonomous systems Future-proof against evolving threats
Real-World Use Cases Healthcare: Workday and IBM Using NIST for Patient Data Protection Workday, a global HR and finance software provider, uses the NIST AI Risk Management Framework to align its internal AI governance processes. Their Privacy and Data Engineering team mapped existing controls to NIST’s Govern, Map, Measure, and Manage functions. They created templates and SOPs to operationalize responsible AI across teams.
IBM also adopted NIST AI RMF. Their Chief Privacy Office led a three-phase audit comparing IBM’s internal Ethics by Design methodology to NIST’s framework. IBM now recommends that government agencies adopt NIST RMF for AI governance.
These efforts help ensure AI systems used in healthcare, HR, patient planning, and data analytics meet ethical and legal standards.
Finance: MITRE ATLAS for Threat Modeling in Fraud Detection Cylance, a cybersecurity firm acquired by BlackBerry, used adversarial inputs to bypass a machine learning malware scanner. This case was documented in the MITRE ATLAS Framework, showing how attackers used public data and reverse engineering to evade detection.
MITRE ATLAS helped security teams understand tactics like model evasion, data poisoning, and prompt injection. It also guided mitigation strategies like retraining models with adversarial samples and tightening API access.
Financial institutions now use MITRE ATLAS to model threats against fraud detection systems, ensuring their AI tools can withstand real-world attacks.
Retail: UiPath Maestro for Agentic AI in Customer Service Abercrombie & Fitch, Johnson Controls, and Wärtsilä use UiPath Maestro, which is built on the MAESTRO framework, to orchestrate agentic AI in customer service and operations.
Johnson Controls automates end-to-end processes using AI agents and robots, improving speed and accuracy. Wärtsilä, a global marine and energy company, integrates agentic orchestration to manage business processes across systems. EY and CGI also partner with UiPath to deploy agentic AI for clients, combining automation, AI agents, and human oversight to deliver scalable customer service solutions.
How to Choose the Right AI Security Framework Selecting the right AI security framework is not a one-size-fits-all decision. The best choice depends on your organization’s AI maturity, regulatory environment, and existing security infrastructure. Here are key considerations:
1. Match the Framework to Your AI Maturity Early-stage AI adoption: Organizations experimenting with AI pilots should start with flexible frameworks like NIST AI RMF, which provide high-level guidance on managing risks without overwhelming technical requirements.
Advanced AI deployment: Companies already running large-scale AI systems may benefit from more specialized frameworks like MITRE ATLAS for adversarial threats or MAESTRO for multi-agent security.
2. Consider Compliance and Regulatory Needs If your industry is heavily regulated (healthcare, finance, government), frameworks that align closely with global standards such as ISO/IEC 23894 or NIST are often the safest choices.
These frameworks map directly to compliance requirements like GDPR, HIPAA, or PCI DSS, helping reduce legal risks.
3. Look at Integration with Existing Tools and Processes Evaluate whether the framework can integrate with your current DevSecOps pipelines, monitoring systems, and governance tools.
For example, MITRE ATLAS aligns well with existing threat modeling tools, making it easier to add AI-specific security without reinventing the wheel.
4. Use Hybrid Approaches if Needed Many enterprises adopt a hybrid strategy, combining elements from different frameworks.
For instance, a financial institution may use NIST for governance while applying MITRE ATLAS for red-teaming AI models.
This layered approach ensures broader coverage across governance, compliance, and active threat defense.
Case Study: Real-Time Compliance and Risk Detection Client: A global expert network platform connecting decision-makers with over one million subject-matter experts.
Challenge: The client’s compliance team manually screened experts for negative news across public sources. This caused delays, backlogs, and inconsistent vetting.
Solution: Kanerika built an AI-powered compliance agent that automated expert profiling, scraped news and social media, and applied rule-based logic to flag risks. The agent generated structured reports with citations and mapped findings to compliance rules.
Impact:
60% faster screening 70% fewer backlog cases 40% reduction in event delays Standardized and auditable risk assessments
Securing AI Systems with Kanerika’s Proven AI Security Framework At Kanerika, we design AI security frameworks that help enterprises protect their models, data, and workflows from evolving threats. Our layered approach combines data governance, risk detection, and compliance automation to secure AI systems across industries.
We use tools like Microsoft Purview to classify sensitive data , detect insider risks, and enforce policies automatically. Our framework supports AI TRiSM principles, making sure every AI model we deploy is transparent, accountable, and aligned with ethical standards. This helps our clients stay compliant with regulations like GDPR, HIPAA, and the EU AI Act.
Our partnerships with Microsoft, Databricks, and AWS allow us to deliver scalable, enterprise-grade AI security solutions. With certifications like ISO 27701, SOC II, and CMMi Level 3, we back our work with proven security and quality standards . Whether you’re working with LLMs, RPA bots, or autonomous agents, our AI security framework is built to adapt and protect.
Partner with us to build a trusted AI security framework that protects your data, ensures compliance, and scales with your enterprise.
FAQs 1. What is an AI security framework? An AI security framework is a structured set of policies, processes, and tools designed to protect AI systems including models, data, and infrastructure from specific risks like adversarial attacks, drift, and non-compliance. Unlike generic cybersecurity standards, it addresses vulnerabilities unique to AI.
2. Why do organizations need AI-specific security frameworks? AI systems can be manipulated through methods like adversarial inputs, prompt injection, or data poisoning—threats that traditional cybersecurity tools don’t fully cover. Frameworks built specifically for AI help organizations manage these risks, remain compliant, and uphold user trust.
3. Which industries benefit most from AI security frameworks? Sectors like finance, healthcare, and retail where AI handles sensitive information, customer decisions, or critical automation, particularly need AI security frameworks. These industries face heightened regulatory and ethical scrutiny.
4. What are some core components covered by AI security frameworks? Common elements include data integrity , threat modeling, adversarial testing, secure model deployment, ongoing monitoring, fairness and bias mitigation, explainability, and compliance controls.
5. What standards and frameworks are available for securing AI? Some widely referenced frameworks include:
1. NIST AI RMF
2. OWASP’s AI Security & Privacy Guide
3. Google’s Secure AI Framework (SAIF)
4. Databricks AI Security Framework (DASF)
5. Framework from ENISA (FAICP)
6. AI TRiSM (Trust, Risk, and Security Management)
These cover lifecycle governance, adversarial risk, and ethical/security best practices.
6. How should enterprises choose the right AI security framework? Evaluate based on your organization’s maturity stage, regulatory demands, and AI infrastructure. Regulated industries may lean toward frameworks like NIST AI RMF, while AI-driven operations using autonomous agents might benefit from layered or specialized models like AI TRiSM or DASF. Often, a hybrid approach combining elements from multiple frameworks is most effective.
What is the security for AI framework? An AI security framework is a structured set of rules, processes, and tools designed to protect AI systems from misuse, adversarial attacks, and compliance risks. Unlike traditional cybersecurity frameworks, it addresses AI-specific vulnerabilities like model drift, adversarial inputs, data leakage, shadow AI, and prompt injection. Key frameworks enterprises use in 2025 include: NIST AI RMF Best for regulated industries needing structured risk management Microsoft AI Security Framework Ideal for Azure users prioritizing responsible AI MITRE ATLAS Focuses on real-world AI attack modeling Databricks DASF Best for data-heavy enterprises with 62 risks and 64 controls mapped MAESTRO Built specifically for autonomous and agentic AI systems With 84% of enterprises experiencing AI-related data leaks (Cisco 2025), implementing the right AI security framework is critical for protecting models, ensuring regulatory compliance with GDPR, HIPAA, and the EU AI Act, and maintaining customer trust.
What are the 5 pillars of AI framework? The 5 pillars of an AI security framework are governance, risk assessment, threat modeling, compliance, and monitoring. While different frameworks organize these slightly differently, most enterprise AI security approaches share these core elements: Governance Policies, accountability structures, and oversight (central to NIST AI RMF and Microsoft’s framework) Risk Assessment Identifying AI-specific threats like model drift and adversarial inputs Threat Modeling Mapping real-world attack methods, as MITRE ATLAS and Databricks DASF do across 62 documented AI risks Compliance Controls Ensuring alignment with GDPR, HIPAA, and the EU AI Act Continuous Monitoring Detecting bias, drift, and shadow AI in production systems Kanerika integrates all five pillars into its AI security framework, helping enterprises deploy AI that is transparent, accountable, and regulation-ready across industries like finance, healthcare, and retail.
What are the main AI frameworks? The main AI security frameworks are NIST AI RMF, OWASP’s AI Security & Privacy Guide, Google’s Secure AI Framework (SAIF), Databricks AI Security Framework (DASF), ENISA’s Framework (FAICP), and AI TRiSM (Trust, Risk, and Security Management). Additionally, MITRE ATLAS focuses on adversarial threats, while MAESTRO addresses multi-agent AI security risks. Each framework serves a different purpose. NIST AI RMF is ideal for early-stage AI adoption and regulated industries like healthcare and finance. MITRE ATLAS suits advanced deployments needing active threat modeling. AI TRiSM covers trust, risk, and compliance holistically. Many enterprises, like those working with Kanerika, adopt a hybrid approach, combining elements from multiple frameworks to address governance, adversarial risks, and compliance requirements simultaneously. Choosing the right framework depends on your AI maturity, regulatory environment, and existing security infrastructure.
What are the 4 types of AI? The 4 main types of AI are reactive machines, limited memory, theory of mind, and self-aware AI. Reactive machines respond to inputs without memory (like chess engines). Limited memory AI learns from past data—this powers most enterprise tools today, including fraud detection and recommendation systems. Theory of mind AI is still emerging, designed to understand human emotions and intentions. Self-aware AI remains theoretical, representing machines with full consciousness. Most AI security frameworks discussed in enterprise contexts, including NIST AI RMF, MITRE ATLAS, and DASF, are built to secure limited memory AI systems, which are the most widely deployed and actively targeted type in production environments today.
What are the 7 main types of AI? The 7 main types of AI are narrow AI, general AI, superintelligent AI, reactive machines, limited memory AI, theory of mind AI, and self-aware AI. These are categorized by capability and functionality. Narrow AI (like ChatGPT or fraud detection tools) handles specific tasks and is what most enterprises deploy today. Limited memory AI learns from historical data to improve decisions, powering systems like autonomous vehicles and recommendation engines. Reactive machines respond to inputs without memory, while general AI can perform any intellectual task a human can. Theory of mind and self-aware AI remain largely theoretical. Superintelligent AI surpasses human intelligence entirely. For enterprises building AI security frameworks, understanding which type of AI you’re deploying matters significantly, as each carries distinct risks around model drift, adversarial attacks, and compliance, areas that frameworks like NIST AI RMF and MAESTRO are specifically designed to address.
What are the three types of security? The three types of security are physical security (protecting physical assets, facilities, and people), cybersecurity (protecting digital systems, networks, and data from cyber threats), and operational security (protecting processes, workflows, and sensitive information from exposure or misuse). In the context of AI security frameworks covered in this blog, all three types apply. Physical security protects AI hardware infrastructure, cybersecurity defends AI models from adversarial attacks, prompt injection, and data leakage, while operational security addresses governance, compliance, and shadow AI risks. Organizations like Kanerika integrate all three layers when building enterprise AI security frameworks, ensuring models remain protected across their entire lifecycle from deployment to monitoring while meeting regulations like GDPR, HIPAA, and the EU AI Act.
What are the 4 types of AI risk? The 4 main types of AI risk are model risk, data risk, operational risk, and compliance risk. Based on the blog’s framework analysis, these break down as: Model Risk AI drift, adversarial inputs, and manipulation that cause wrong decisions over time Data Risk Data leakage, poisoning, and biased training data that corrupt outputs Operational Risk Shadow AI, prompt injection, and model theft disrupting business functions Compliance Risk Failing to meet regulations like GDPR, HIPAA, or the EU AI Act Cisco’s 2025 AI Security Report confirms 84% of enterprises have already experienced data leaks, proving these risks are active threats, not theoretical ones. Kanerika’s AI security framework is specifically built to address all four risk types across LLMs, RPA bots, and autonomous agents, keeping enterprises protected and compliant.
Which AI is best for security? No single AI is best for security the right choice depends on your use case, industry, and threat profile. Based on established frameworks, here’s what works best by scenario: Regulated industries (healthcare, finance): NIST AI RMF provides structured, compliance-ready governance aligned with HIPAA and GDPR Threat modeling and adversarial attacks: MITRE ATLAS is purpose-built for identifying model evasion, data poisoning, and prompt injection risks Data-driven enterprises: Databricks DASF covers 62 risks with 64 actionable controls Autonomous/agentic AI systems: MAESTRO is designed specifically for multi-agent security risks Most enterprises use a hybrid approach combining NIST for governance with MITRE ATLAS for active threat defense. Kanerika, for example, layers Microsoft Purview, AI TRiSM principles, and compliance automation to secure AI systems across industries with certifications like ISO 27701 and SOC II. The best AI security strategy isn’t one tool it’s the right framework matched to your risk profile.
What are 5 applications of AI? AI has five major applications across industries: fraud detection, medical diagnostics, customer service automation, autonomous systems, and predictive analytics. Fraud Detection AI analyzes transaction patterns in real-time to flag suspicious activity in banking and finance . Medical Diagnostics AI models assist doctors in identifying diseases through imaging and patient data analysis. Customer Service AI-powered chatbots and virtual assistants handle queries, though as seen with Lenovo’s GPT-4 chatbot incident, they require proper security frameworks. Autonomous Systems AI drives RPA bots and agentic AI for business process automation. Predictive Analytics AI forecasts trends in retail, supply chain, and operations. Each application introduces unique security risks like data leakage and adversarial attacks, making structured AI security frameworks essential. Kanerika helps enterprises deploy these applications securely while maintaining compliance with GDPR, HIPAA, and the EU AI Act.
What is an example of AI security? An example of AI security is prompt injection protection, where safeguards prevent attackers from manipulating AI chatbots into revealing sensitive data or bypassing controls. Real-world cases from the blog illustrate this clearly—Lenovo’s GPT-4-powered chatbot was compromised in 2025, exposing customer data due to inadequate AI security measures. Similarly, Google’s Salesloft AI agent suffered a mass data theft incident, triggering emergency shutdowns. Other practical AI security examples include adversarial input detection (blocking manipulated data designed to trick models), shadow AI monitoring (identifying unapproved AI tools used without oversight), and model drift detection (catching when AI begins making incorrect decisions over time). Companies like Kanerika implement these protections using tools like Microsoft Purview for data classification and AI TRiSM principles, ensuring models remain secure, compliant, and trustworthy across regulated industries like healthcare and finance.
What are the three types of security systems? The three types of security systems are physical security (cameras, locks, access control), cybersecurity (firewalls, encryption, intrusion detection), and AI security frameworks (governance, adversarial threat modeling, and compliance controls). While traditional security systems protect infrastructure and networks, AI security frameworks address unique risks like model drift, prompt injection, data leakage, and shadow AI that conventional tools cannot handle. As AI becomes embedded in fraud detection, healthcare diagnostics, and customer service, enterprises increasingly need all three layers working together. Organizations like Kanerika help businesses implement structured AI security frameworks, combining physical and cyber protections with AI-specific governance to ensure compliance with regulations like GDPR, HIPAA, and the EU AI Act across their full technology stack.
What are AI security tools? AI security tools are software solutions designed to protect AI systems from threats like adversarial attacks, data poisoning, prompt injection, and model theft. Unlike traditional cybersecurity tools, they address vulnerabilities unique to machine learning models. Common AI security tools include: Microsoft Purview classifies sensitive data, detects insider risks, and enforces compliance policies automatically Threat modeling tools identify AI-specific risks across the model lifecycle Adversarial testing platforms simulate attacks to expose model weaknesses Monitoring systems detect model drift and anomalous behavior in real time Compliance automation tools map AI activity to regulations like GDPR, HIPAA, and the EU AI Act Kanerika leverages tools like Microsoft Purview alongside AI TRiSM principles to build enterprise-grade AI security frameworks. With certifications like ISO 27701 and SOC II, these tools help organizations secure models, protect data, and stay audit-ready across industries including finance, healthcare, and retail.
What is the NIST security framework for AI? The NIST AI Risk Management Framework (AI RMF) is a structured governance and security framework designed to help organizations identify, assess, and manage AI-specific risks across the entire AI lifecycle. It is built around four core functions: Govern, Map, Measure, and Manage. Key features of the NIST AI RMF include: Lifecycle-based risk assessment covering AI development through deployment Trustworthy AI principles emphasizing fairness, transparency, and accountability Structured controls to reduce compliance and operational risk It is best suited for highly regulated industries like healthcare, finance, and government, and for enterprises needing a compliance-focused approach aligned with regulations like HIPAA and the EU AI Act. Organizations like Kanerika leverage NIST AI RMF principles to build enterprise-grade AI security frameworks that ensure models remain transparent, accountable, and compliant throughout their lifecycle.
