Defending Against Evolving AI Threats Through Intelligent Monitoring
AI security requires more than technical safeguards. RedStream monitors real-world narrative threats to protect your models, users, and regulatory compliance.
RedStream’s RS-7 framework has undergone comprehensive adversarial testing across multiple frontier and open-weight AI models. The results revealed consistent failure patterns that are often missed by traditional benchmarks—validating RS-7 as a structured, model-agnostic evaluation system for AI safety, misuse exposure, and risk classification.
LLM Architectures Evaluated
Spanning frontier systems and open-weight models across multiple alignment strategies
Adversarial Scenarios Executed
Systematic testing with multi-variant prompts across all RS-7 risk categories, spanning multiple risk levels
Safety and Alignment Vulnerabilities
Indicative of systemic safety breakdowns under adversarial pressure
The results exposed systemic risks in both small (<1B) and large (>5B) models, with red-level responses triggered in nearly 30% of runs. These vulnerabilities are aligned with ATLAS tactics such as AML.TA0005 (Execution), AML.TA0011 (Impact), and AML.TA0004 (Initial Access).
Emerging regulations are mandating standardized adversarial testing for AI systems:
Requires documented adversarial testing of high-risk AI systems, with emphasis on foreseeable misuse and risk mitigation.
Mandates security testing for AI systems with potential national security, public health, or safety implications.
Recommends comprehensive risk management practices including independent adversarial testing throughout the AI lifecycle.
RedStream's RedStream Risk Categories (RS-7) framework is designed to help organizations prepare for upcoming regulatory requirements, with a focus on documenting security testing and risk mitigation strategies.
Starting in 2025, major regulatory frameworks will begin requiring formal adversarial testing for AI systems.
Potential consequences of non-compliance:
Regulatory penalties
Legal liability
Operational restrictions
Reputational damage
RedStream scenarios are grounded in open-source intelligence (OSINT) on real-world threat actor behavior patterns:
RedStream maps each detected behavior to the MITRE ATLAS™ framework, an industry-standard model for adversarial AI behavior.
Emerging narrative detected
Vaccine misinformation
Vaccine misinformation narratives showing 43% increase
Disinformation campaigns, narrative manipulation, and coordinated perception warfare techniques
Radicalization vectors and violent content generation tactics used to manipulate AI systems
Coordinated manipulation of public discourse through AI system exploitation
LLM jailbreak techniques and policy circumvention strategies
Our threat library is continuously updated based on monitoring adversarial spaces and real-world attainment of exploitation objectives.
RedStream is built to simulate how adversarial narratives interact with generative AI systems—testing for model-specific vulnerabilities using real-world disinformation tactics.
Our system follows a structured, multi-stage process:
We collect and analyze high-risk information artifacts from multiple sources including social media platforms, extremist forums, and information operations campaigns. Our methodology includes both real-world OSINT collection and synthetic narrative generation for training environments.
Each narrative is tested against a custom-built suite of adversarial prompt scenarios across seven RedStream Risk Categories (RS-7)—covering behavior exploitation, security bypass, misinformation reinforcement, and more.
Model outputs are analyzed and scored using the RS-7 framework to identify where and how systems are most at risk—whether through breakdowns in reasoning, content control, or threat modeling blind spots.
Each detected vulnerability is tied to a structured adversarial tactic model, helping analysts trace how and why the failure occurred through the RedStream Risk Categories.
The result is a clear, structured risk profile using the RS-7 classification that links high-level model weaknesses to specific, testable behaviors—enabling compliance, mitigation planning, and continuous monitoring.
Comprehensive visualization of narrative threat assessment across the seven RedStream risk categories:
This dashboard translates complex threat data into a clear, visual assessment across all RS-7 risk categories—making it easy to spot model vulnerabilities and prioritize security without needing a technical background.
RedStream combines expert-driven risk frameworks with structured AI security testing to help organizations stay ahead of emerging narrative-based threats.
RedStream integrates structured adversarial testing with leading industry frameworks to produce actionable model risk profiles. Our system bridges narrative-driven threats and technical AI vulnerabilities through a dual-layered architecture: RS-7 Risk Categories for high-level classification, and mapped MITRE ATLAS adversary tactics for granular traceability.
RedStream's RS-7 framework interprets risk behaviorally; MITRE ATLAS provides the forensic mapping to specific adversarial techniques.
Existing solutions primarily address:
Our platform extends protection to include:
RedStream is constantly evolving to address new threat vectors and enhance our detection capabilities:
MITRE ATLAS™ (Adversarial Threat Landscape for Artificial Intelligence Systems) is the industry-standard knowledge base for AI/ML security threats. It provides a comprehensive taxonomy of adversary tactics and techniques specifically targeting AI systems.
Our framework is fully aligned with MITRE ATLAS—the globally recognized knowledge base of adversary tactics against AI systems
RedStream identifies not just what attacks occur—our dynamic vulnerability scoring, OSINT monitoring, risk assessment shows how they evolve in response to real-world events and why adversaries deploy specific narratives—delivering actionable intelligence where traditional technical audits fall short.
RedStream's enhanced methodology goes beyond simple pass/fail testing. Our post-test clustering engine identifies patterns across multiple narrative tests, linking model behaviors to their root causes. This approach reveals systemic vulnerabilities that individual tests might miss, enabling more targeted mitigation strategies and comprehensive security coverage across the full threat landscape.
RedStream’s narrative detection and adversarial testing framework supports realistic simulation of digital information environments. Built for use in training, red teaming, and influence operations analysis, the system provides synthetic content generation, scenario automation, and pattern recognition capabilities for both live and offline use.
Our clustering and narrative extraction modules reduce manual scenario development time while increasing relevance and complexity. Real-world OSINT sources feed directly into structured scenario templates that reflect current adversarial behaviors across social platforms.
RedStream’s prompting system is designed to generate calibrated synthetic behaviors based on known adversarial narrative patterns. The RS-Prompt training structure is built to understand and replicate narratives emerging from Telegram campaigns, Twitter/X threads, and cross-platform coordination—mirroring tactics observed in real-world influence operations, including state-linked disinformation and decentralized propaganda flows.
Our RS-7 framework was built to evaluate LLMs based on measurable failure modes using an adversarial testing methodology. By applying the same risk categories, RS-7 can be adapted to assess performance in scenarios where participants are tasked with decision-making under cognitive or informational stress, providing insight into tactics, techniques, and overall strategic effectiveness.
Real-time clustering enables RedStream to surface shifts in coordination tactics, messaging evolution, and threat signature convergence—useful for dynamic scenario updates or post-exercise analysis. The system is modular, lightweight, and platform-agnostic—built to run in both secure cloud environments and air-gapped systems without additional infrastructure dependencies.
Early warning system for emerging narrative threats with customizable risk thresholds
Targeted security improvements based on RS-7 risk profiles and industry best practices
Documentation templates aligned with EU AI Act, NIST RMF, and US Executive Order requirements
Secure connection to existing security platforms and GRC systems
RedStream is developing a platform that addresses critical security gaps in AI systems, with a focus on narrative-based threats that traditional technical solutions overlook.
Continuous monitoring of emerging narrative threats across multiple sources, providing early warning of potential exploitation vectors before they reach your AI systems.
Identifies patterns and relationships between seemingly disparate narrative threads, enabling more comprehensive threat assessment than isolated technical scanning.
Comprehensive alignment with industry-standard security taxonomies, enabling standardized assessment and documentation of AI vulnerabilities and threats.
Structured reporting and assessment tools designed to meet emerging regulatory requirements for AI adversarial testing and risk management.
RedStream evolves through phased development, with new features prioritized, tested, and released based on operational value and stakeholder input.
RedStream is exploring custom, on-premises infrastructure to enable secure processing of sensitive narrative data. While development is ongoing, our roadmap prioritizes data control and containment, with design concepts focused on:
We aim to host testing data locally, minimizing reliance on third-party cloud providers wherever possible.
We are evaluating systems that reduce external dependencies to strengthen operational security.
Our goal is to build handling procedures aligned with high-security use cases and evolving regulatory frameworks.
This infrastructure vision will guide RedStream's approach to handling sensitive content while meeting the security requirements of enterprise and mission-critical environments.
As the founder of RedStream, Tanner O'Donnell combines experience in AI security evaluation, terrorism studies, and open-source intelligence gathering.
Currently pursuing a Master's degree in Security and Terrorism Studies at the University of Maryland's START Consortium, where Tanner focuses on emerging technologies and their intersections with extremist narratives. He graduated from Hampshire College in 2020.
Tanner has participated in structured red teaming exercises against frontier LLM systems as a counterterrorism specialist, with a focus on high-risk scenarios. He has also evaluated prototype LLM platforms developed by Palantir, IBM, and others as part of an initiative led by the Defense Innovation Unit of the Department of Defense.
As part of his undergraduate thesis project, Tanner conducted extensive research on how online platforms facilitated the coordination of violence through a case study on the 2017 "Unite the Right" rally in Charlottesville. This work examined how extremist groups used platforms like Discord to organize, analyzing leaked chat logs to identify patterns of coordination that preceded physical violence.
Tanner first worked with AI tools in 2019 as an intern with the Syrian Archive and VFRAME. He conducted OSINT research on tools for human rights documentation in conflict zones. His work included contributing to visual guides and developing training materials to support machine learning visual recognition systems for identifying explosive remnants of war.
Tanner has produced analytical products following intelligence community standards (ICD-203), providing him with practical experience in structured reporting methodologies and compliance-focused documentation.
RedStream is being developed through a careful, iterative process that combines security expertise with technical innovation. Our methodology continues to evolve as we refine our approach to AI threat detection.
RedStream’s testing and simulation framework has benefited from peer feedback within the University of Maryland’s START Consortium. Their ongoing research in narrative manipulation, synthetic social media, and influence operations has informed parts of our development process—particularly around simulation realism and adversarial behavior modeling.
Connect with us to learn more about our approach to narrative-based AI security and upcoming platform features.
RedStream is currently in active development with selected partnerships and pilot programs.
For collaboration inquiries:
info@redstream.ai