The 2026 CrowdStrike Global Threat Report details how AI accelerates adversaries and reshapes the attack surface. According to the report, AI-powered cyberattacks are escalating at an unprecedented rate by 89% year-over-year, stealing credentials and avoiding detection.
As organizations rapidly integrate AI into critical business processes, the attack surface is only expected to grow further, where legal and security frameworks can’t keep up. Traditional frameworks aren’t scalable, lack the capacity to tackle evolving security challenges, and weren’t designed to address the unique vulnerabilities of today’s Agentic AI, generative AI, and machine learning (ML) systems.
AI models and systems today amass massive volumes of datasets. A single vulnerability can expose sensitive data in the pipeline, resulting in financial and reputational loss. This is where MITRE ATLAS provides a structured framework to identify, understand, and mitigate threats across the AI lifecycle.
What is MITRE ATLAS?
MITRE is a nonprofit research and development organization based in the United States that works closely with government agencies on technology, security, and defense problems.
MITRE Adversarial Threat Landscape for AI Systems (ATLAS) is a globally accessible, living knowledge base of adversary tactics and techniques based on real-world attack observations and realistic demonstrations from AI red teams and security groups.
ATLAS aims to raise community awareness and readiness for addressing these unique threats, vulnerabilities, and risks in the broader AI assurance landscape. ATLAS is modeled after the MITRE ATT&CK framework, and its tactics, techniques, and procedures (TTPs) are complementary to those in ATT&CK.
Why MITRE ATLAS Matters for AI Security
Unlike legacy systems, today’s Agentic AI systems introduce fundamentally complex security challenges. Traditional tools and frameworks aren’t equipped to handle today’s AI challenges, making an organization’s current security practices inadequate and riddled with vulnerabilities.
This is where MITRE ATLAS provides key adversary tactics and techniques to manage AI security. It does so by:
A. Providing a Comprehensive View of the AI Landscape
ATLAS provides detailed visibility to teams across the organization regarding their current security posture and the AI risks they may be susceptible to. Traditional security practices lack the capability to provide granular insights, identify blind spots, and detect adversary attacks such as data poisoning, model theft, etc.
B. Building a Roadmap of Threats
Threat identification and analysis are core to patching vulnerabilities and future attacks. ATLAS provides a structured roadmap of the threat vector, where teams can clearly build a roadmap of vulnerable areas, whether in the development or deployment phase. It also sheds light on which attacks the business might incur and the types of tactics attackers might use.
Most teams tend to operate in silos, with limited to no collaboration with others and no uniform language that can help with adversarial attacks. ATLAS builds on the framework of common grammar, where teams from multiple departments can collaborate more effectively.
D. Embedding Security Across the AI Lifecycle
AI security is crucial to secure the entire AI lifecycle, not just the initial development stages. It extends to deployment and post-deployment, where AI systems perform in real-world settings. At its core, ATLAS’s knowledge-based framework is built around the principle of understanding, detecting, and mitigating real-world AI-specific threats.
E. Fostering a Culture of Proactive AI Security
There’s no AI security without visibility into who handles the data being fed to the model (accountability), what data is being used (its accuracy), and human intervention to regulate the model where necessary (continuous oversight). An organized structure demonstrates clarity, fostering a culture of proactive security rather than a reactive approach.
Components of the MITRE ATLAS Framework
MITRE ATLAS is structured to make teams easily understand and assess AI-related threats. Key components of the MITRE ATLAS framework include:
A. Tactics
Tactics define the end goal of the attacker as to why they are conducting an attack (their purpose). It walks down the attacker’s entire journey from inception to actually causing damage. It includes phases like obtaining preliminary information, gaining access, navigating and avoiding detection protocols, and then actually stealing data and causing damage.
B. Techniques
Techniques define how attackers conduct an attack. It outlines the steps an attacker takes, such as duping the AI model by giving misleading inputs, poisoning the datasets given during the training phase, copying the model code and replicating it, and extracting sensitive data from the dataset.
C. Case Studies
To support Tactics and Techniques, ATLAS provides real-world case studies and examples that demonstrate ground-level scenarios with research documents to support the findings. These reports and analyses enable teams to understand firsthand what AI attacks actually look like in practice, not just in theory.
D. Mitigations
The framework builds up to the mitigation stage, where ATLAS recommends methods and approaches to defend the AI lifecycle against evolving AI threats. It outlines how teams can effectively detect incoming threats as well as detect if they have already infiltrated systems, and most importantly, how to respond to such threats.
E. AI System Lifecycle Coverage
ATLAS is a comprehensive guide that covers end-to-end security, meaning it considers the entire AI lifecycle from model development to deployment, as well as the data being fed to train the AI model, all the way to its outputs.
Common AI Threats Identified by MITRE ATLAS
Although MITRA ATLAS identifies several AI threats, the most common AI threats identified by the framework include:
A. Data Poisoning
This is one of the most common types of cyberattacks where the attacker manipulates or corrupts training data, making the model train on misleading and inaccurate data, essentially poisoning the dataset. This results in jeopardized security and inaccurate outputs.
B. Adversarial Examples
This is where an attacker injects inputs to trick the AI model. While they might appear to be harmless and normal to a human oversight team, the model interprets them rather differently, leading it to make a wrong decision when providing an output.
Attackers steal the model altogether by repeatedly querying it and learning from its responses. By analyzing how the model is responding, it enables them to build a similar model, thereby further improving it and giving it a competitive edge over others.
D. Model Inversion
The attackers exploit a trained AI model's API and reverse engineer, working from the model’s outputs all the way to the inputs. This is done in an effort to reconstruct sensitive data fed during the training stage or accumulated by the model during the prompt input stage.
E. Evasion Attacks
Evasion attacks are done to evade detection. This is where the attackers discreetly change the input data instead of targeting the model altogether. This tactful method is just enough to bypass anomaly detection protocols while still achieving a malicious outcome.
F. Supply Chain Attacks
Attackers go the extra mile and start targeting vulnerable third-party services linked to the model. This results in the attacker gaining access to services containing sensitive datasets and model training data, impacting the overall system.
G. Data Leakage
As AI models process large volumes of datasets and handle a multitude of prompts, they tend to inadvertently expose sensitive data, resulting in data leakage through the model’s output.
H. Denial of Service (DoS)
Attackers overburden the entire AI system with heavy traffic, resulting in complete system collapse. This results in system disruption and downtime, which can take a lot of time to recover. In the meantime, the attackers can exploit vulnerabilities, causing further damage.
How Securiti Helps Organizations Build a Resilient AI Security and Governance Posture
MITRE ATLAS is a robust framework that highlights the growing range of adversarial threats, tactics and attacks that increasingly target AI technologies. It stresses the importance of stronger governance and control as enterprises adopt generative AI, LLMs, and Agentic AI systems.
AI Agents are becoming increasingly autonomous and capable of making independent decisions without human intervention. The new threat landscape requires organizations to adopt measures that minimize sensitive data exposure, regulate AI behavior and ensure regulatory compliance.
Securiti helps organizations build a resilient data security, privacy, and AI governance posture by providing unified visibility and policy-based controls across the data and AI lifecycle. Through centralized governance, real-time guardrails, and context-aware controls for enterprise data, prompts, inputs, outputs, and agent actions, organizations can reduce AI-related risks while maintaining compliance, trust, and operational control as they scale AI adoption.
Request a demo to learn more.
