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FAQ

Here you'll find answers to the most common questions about AllTrue.ai, our platform, and how we help you manage AI risks with confidence.

  • AI TRiSM stands for Artificial Intelligence Trust, Risk, and Security Management. It is a framework for governing AI systems in a way that ensures they are secure, trustworthy, resilient, and compliant with corporate risk policies and regulatory requirements.

     

    While AI TRiSM covers trust, risk, and compliance broadly, it places special emphasis on the security and risk governance of AI systems and their outputs, protecting organizations against technical threats, business risks, and legal exposure.

    Pillar
    Focus in AI TRiSM
    Trust

    Establishing confidence that AI behaves predictably, ethically, and reliably under governance policies.

    Risk Management

    Proactively identifying and mitigating risks from AI misuse, regulatory violations, or unintended consequences.

    Security

    Protecting AI assets (models, data, agents, APIs) against attacks like model theft, data poisoning, adversarial inputs, and ensuring supply chain security for AI components.

    Governance (GRC)

    Embedding AI lifecycle oversight into corporate risk governance frameworks, ensuring responsible use, regulatory reporting, and executive accountability.

    Specific examples in a security and GRC context:

    • AI attack prevention: Defending models against adversarial attacks, membership inference, and model inversion.

    • Security audits for AI: Verifying integrity and confidentiality of AI pipelines (training, deployment, inference).

    • Regulatory compliance: Meeting strict governance standards like the EU AI Act, ISO 42001, NIST AI Risk Management Framework (AI RMF).

    • Third-party risk management: Vetting external AI services or models to ensure supply chain security.

    • Incident response for AI: Having structured response plans for AI-specific failures or breaches.

    Why it matters to security and GRC:

    • Security breaches in AI systems can lead not just to data leaks but to manipulation of critical business processes.

    • Unmanaged AI risks can lead to financial losses, reputational damage, and regulatory penalties.

    • Corporate risk governance now often requires AI-specific controls, evidence of model testing, and continuous risk monitoring at the board or executive level.

    AI TRiSM ensures AI doesn't become a hidden risk inside the enterprise — it becomes a managed, secured, and governed asset.

  • No, AI TRiSM is not exactly the same as AI Security – but AI Security is a core part of AI TRiSM.

    Here’s the relationship:

    Term
    Meaning
    Scope
    AI Security

    Protecting AI systems (models, data, pipelines, LLMs, agents) against threats like attacks, theft, tampering, and adversarial manipulation.

    Technical and operational security: attack surface reduction, secure model and LLM deployment.

    AI TRiSM

    Trust, Risk, and Security Management — a broader governance and management framework that covers not just security but also trust, risk, compliance, fairness, transparency, accountability, and lifecycle management of AI systems.

    Security + Risk Management + Trust + Governance / Compliance.

    It's enterprise-wide.

    Simple way to think about it:

    • AI Security is a subset of AI TRiSM.

    • AI TRiSM includes AI Security, risk governance, regulatory compliance, ethics and fairness and model management.

    In short:

    All AI TRiSM involves security, but not all AI Security covers everything needed for full AI TRiSM.

  • You should care about AI TRiSM because without it, AI systems become unmanaged risks – risks that can damage your business, expose you legally, harm your customers, and undermine trust.

    Here’s a structured breakdown:

    1. AI Systems Create New, Hidden Risks:

    • AI can fail in unexpected ways.

    • If you're not monitoring AI properly, errors can scale automatically across customers, transactions, or decisions before you even notice.

    • AI risks are harder to detect than traditional IT risks because models change behavior dynamically.

    2. Regulators Are Moving Fast:

    • New regulations like the EU AI Act, NIST AI RMF, ISO 42001, and sector-specific rules (like healthcare, finance) demand proof that AI is secure, transparent, and governed.

    • You can face fines, lawsuits, or penalties if your AI causes harm or violates new rules.

    • Without AI TRiSM, you may fail audits or lose deals with security-conscious customers.

    3. AI Security Threats Are Real:

    • AI models can be:

      • Stolen (model extraction attacks)

      • Poisoned (training data attacks)

      • Manipulated (adversarial inputs causing bad decisions)

    • Without strong AI security (which TRiSM embeds), you risk intellectual property theft, data breaches, or operational sabotage.

    4. Trust is a Competitive Advantage:

    • Customers, investors, and partners increasingly choose companies they trust to handle AI responsibly.

    • If your AI is accountable, it becomes a strategic asset rather than a reputational risk.

    • Companies with good AI governance can innovate faster because they manage risks systematically instead of reacting chaotically when something breaks.

    5. Board and Executive Accountability:

    • AI-driven decisions (like approving loans, diagnosing diseases, detecting threats) are now material risks — meaning boards are expected to oversee AI risk the same way they oversee cybersecurity and financial risk.

    • Without AI TRiSM, executives and boards may be personally liable under emerging regulations.

    Short summary:

    Without AI TRiSM, you’re running AI without a seatbelt.
    With AI TRiSM, you turn AI into a secure, governable, compliant business enabler.

  • Multiple groups across an organization should care deeply about AI TRiSM — not just the AI team.

    Here's a structured view:

    Role
    Why They Should Care About AI TRiSM
    CEO / Board

    AI risks (security breaches, ethical failures, compliance violations) are now material business risks. Boards are increasingly expected to oversee AI governance the same way they oversee cybersecurity, ESG, and financial risk.

    Chief Risk Officer (CRO)

    AI introduces new operational, legal, and reputational risks that must be integrated into the Enterprise Risk Management (ERM) framework. TRiSM ensures risk policies include AI-specific controls.

    Chief Information Security Officer (CISO)

    AI models, pipelines, and APIs need security hardening just like traditional IT systems. Attackers now specifically target AI. CISO teams must own or oversee AI security programs.

    Chief Compliance Officer (CCO)

    New laws (EU AI Act, NIST AI RMF, ISO 42001) impose specific compliance duties for AI systems. TRiSM helps track and prove that AI use meets regulatory and internal policy requirements.

    Chief Data Officer (CDO)

    AI models depend on data quality, privacy, and governance. TRiSM aligns AI model management with data governance policies, reducing bias and data leakage risks.

    General Counsel / Legal

    AI decisions can create legal liability (e.g., discrimination claims, unfair practices). TRiSM provides the evidence and documentation needed to defend or mitigate liability.

    Chief Technology Officer (CTO) / Head of AI

    AI developers need secure, compliant pipelines. TRiSM frameworks guide the secure design, monitoring, and maintenance of AI models throughout their lifecycle.

    Internal Audit

    Auditors must review AI systems just like financial or IT systems. TRiSM gives audit teams a framework and evidence trail for model validation and controls testing.

    Product Owners / Business Units

    Products and services using AI must be trustworthy, safe, and reliable. TRiSM helps ensure that customer-facing AI doesn't introduce unanticipated risks to revenue or brand.

  • AI TRiSM applies broadly across multiple types of AI, not just one kind.

    Here’s a breakdown:

    Type of AI
    Examples
    How AI TRiSM Applies
    Machine Learning (ML)

    Predictive models, fraud detection, recommendation engines

    TRiSM ensures the model is secure, fair, explainable, and monitored for drift or bias over time.

    Generative AI (GenAI)

    Chatbots (like ChatGPT), image generators (like DALL-E), code assistants

    TRiSM addresses hallucination risks, prompt injection attacks, and toxic outputs.

    Natural Language Processing (NLP)

    Sentiment analysis, translation, text summarization

    TRiSM ensures secure handling of sensitive data, protects against inference attacks, and reduces biased language outputs.

    Autonomous Systems and Agentic Systems

    Ai agents, robotic process automation (RPA) and tools connected to inference

    TRiSM ensures safe decision-making, fault tolerance, security against tampering, and compliance with safety standards.

    Reinforcement Learning (RL)

    Trading bots, dynamic recommendation engines, industrial robotics

    TRiSM manages unpredictable behaviors, reward hacking risks, and ensures continuous oversight and retraining.

    Decision Systems / Expert Systems

    Underwriting systems, risk scoring engines, eligibility algorithms

    TRiSM applies transparency, security auditing, and explainability controls to automated decisions.

    In short:

    AI TRiSM covers any AI system that makes, assists, or influences decisions — whether it's statistical, generative, autonomous, or rules-based.​

    ​If it can impact people, finances, operations, security, or compliance, it falls under the AI TRiSM umbrella.

    A good way to summarize:

    If data goes in, a decision or action comes out, and it could affect the company, customers, or compliance  ➔  You need AI TRiSM.

  • Some key differences:

    • AI systems can fail silently and subtly instead of crashing visibly.

    • Attackers have new ways to manipulate outputs without breaching infrastructure.

    • Governance maturity around AI is far lower than traditional IT.

    Quick analogy:

    Traditional IT risk is like securing a house: you protect the doors, windows, and alarms. AI risk is like securing a living organism: it's constantly changing, adapting, and can develop vulnerabilities inside itself unless you constantly monitor and intervene.

    A few more examples:

    Aspect
    Traditional Technology Risk
    AI Risk Surface
    Predictability

    Systems behave deterministically — given input X, you expect output Y.

    AI systems behave probabilistically — same input X might produce different Y’s (especially in GenAI and ML), making failure modes harder to predict.

    Failure Modes

    Bugs, outages, breaches are usually binary (system works or it doesn’t).

    AI failures are subtle, cumulative, and gradual.

    Attack Surface

    Focus on software vulnerabilities, network security, and access control.

    Expanded attack surface: model inversion, data poisoning, adversarial examples, prompt injection (in GenAI), model theft. New threats against data, models, and outputs.

    Explainability

    Easy to trace cause-and-effect (logs, stack traces, user actions).

    AI outputs can be opaque or non-explainable — even developers often can’t fully explain why a model made a certain decision.

    Change Risk

    Change is mostly manual — developers push patches, upgrades.

    AI models self-learn or drift over time without human visibility unless monitored (dynamic systems).

    Governance Readiness

    Well-established frameworks (SOX, GDPR for data, NIST for cybersecurity).

    Emerging governance — frameworks like the EU AI Act, ISO 42001, NIST AI RMF are new and evolving. Many companies have immature or no AI governance processes.

    Impact Scope

    Tech failures impact systems, data, maybe finances.

    AI failures impact human rights, fairness, safety, reputation, and regulatory exposure — potentially much more socially sensitive and public.

  • The regulatory landscape for AI is evolving very rapidly — and it's moving from light guidance to strict, enforceable laws with real penalties.

    Here’s a clear breakdown of what’s happening:

    1. From Voluntary to Mandatory:

    • Early AI guidelines (like the EU Ethics Guidelines for Trustworthy AI, 2019) were voluntary — focused on principles (fairness, transparency, accountability).

    • Now governments are moving to binding laws with audits, fines, and criminal penalties for non-compliance.

    2. Major New Regulations Taking Shape:

    Region
    Regulation
    Key Focus
    European Union

    EU AI Act (2024-2026 enforcement)

    World's first comprehensive AI law. Categorizes AI systems by risk. High-risk AI (like finance, healthcare, employment AI) must meet strict requirements: transparency, human oversight, risk management, cybersecurity, documentation, and registration.

    United States

    Executive Order on AI Safety (2023),

    NIST AI Risk Management Framework (2023), 

    State laws (e.g., Colorado, California)

    No federal law yet, but strong executive actions. Push for voluntary standards via NIST AI RMF. States starting to regulate bias audits (e.g., automated hiring tools in NYC/Illinois). Financial regulators are also stepping in individually.

    Canada

    AIDA (Artificial Intelligence and Data Act) (proposed)

    Would require companies to manage AI risks proportionally to their impact and subject high-risk systems to audits and penalties.

    China

    Generative AI Measures (2023)

    Requires all AI outputs to be "socially positive," prohibits unauthorized generation of deepfakes, mandates security assessments for certain AI uses.

    United Kingdom

    AI White Paper approach (2023)

    Sector-based regulation instead of one sweeping AI law. 

    Emphasis on aligning existing sector regulators (like healthcare, finance) to enforce AI rules.

    Global Bodies

    G7, OECD, UN AI Governance proposals

    International guidance and draft frameworks to align cross-border AI safety, ethics, and security expectations.

    3. Enforcement is Getting Real - for example:

    • EU AI Act: Fines up to €35 million or 7% of global turnover for violations.

    • NYC Local Law 144: Mandatory bias audits for automated employment decision tools.

    • SEC / FTC / CFPB (U.S.): Investigating misleading use of AI in advertising, finance, and consumer products.

    Summary:

    The era of 'move fast and break things' for AI is over. AI is now being treated like financial products, medical devices, or critical infrastructure — subject to regulation, audits, and liability.

    Companies that build AI governance now (through frameworks like AI TRiSM) will be in a much better position to comply, innovate, and avoid fines.

  • The AllTrue.ai TRiSM Hub is a comprehensive platform designed to manage the Trust, Risk, and Security Management (TRiSM) aspects of artificial intelligence (AI) systems. It provides organizations with tools to ensure that AI applications are secure, compliant, and safe throughout their lifecycle.​

    Key Features of the AllTrue.ai TRiSM Hub:

    • AI Asset Inventory: Automatically discovers and maintains a real-time catalog of all AI resources within an organization, including models, pipelines, and applications.

    • Security Controls: Implements guardrails, penetration tests, and runtime controls to protect AI systems from misuse and attacks, ensuring their integrity.​

    • Risk Management: Evaluates AI systems against operational, regulatory, and reputational risks at every stage of development and deployment.​

    • Regulatory Compliance: Provides prebuilt assessments and auditor-ready reports to facilitate swift compliance with evolving AI regulations, such as the EU AI Act and ISO 42001.

    • Activity Monitoring: Offers continuous monitoring of AI applications to detect drift, measure safety, and track key risk indicators, maintaining user trust.​

    • Supply Chain Risk Management: Ensures that all external AI components meet security and compliance standards, addressing risks associated with third-party AI integrations.

    By integrating these features, the AllTrue.ai TRiSM Hub enables organizations to innovate confidently with AI, ensuring that their AI applications are secure, compliant, and aligned with industry best practices.

  • The AllTrue.ai TRiSM Hub is the “fastest path to safe AI” because it automates and orchestrates the critical controls that companies would otherwise need to build manually — saving time, reducing risk, and ensuring compliance immediately rather than after lengthy custom projects.

    ​

    Specifically, it accelerates safe AI deployment in these ways:

    Reason
    How TRiSM Hub Achieves It
    Instant Visibility

    Automatically inventories all your AI models, pipelines, and systems — you don’t need months of discovery work to understand where your risks are.

    Prebuilt Security and Risk Controls

    Delivers ready-to-use security guardrails, penetration tests, supply chain risk checks, and runtime monitoring — you don't have to design your own risk frameworks from scratch.

    Automated Compliance Readiness

    Provides built-in mappings to regulations like EU AI Act, ISO 42001, NIST AI RMF — with audit-ready reporting — avoiding costly manual compliance projects.

    Continuous Monitoring from Day 1

    Launches real-time monitoring for model drift, safety degradation, and attack attempts, ensuring that AI doesn't silently become dangerous over time.

    End-to-End Coverage

    Covers the entire AI lifecycle: from development to deployment to post-production monitoring — meaning you don’t leave gaps that could create slowdowns or vulnerabilities later.

    Faster Risk Identification and Remediation

    With integrated risk scoring and alerts, you find and fix issues before they cause harm — cutting the response time dramatically.

  • At the core, they are one and the same. IBM Guardium AI Security is the name of the IBM OEM version based on the AllTrue.ai TRiSM Hub. Additionally, Guardium AI Security is integrated with IBM Watsonx.Governance with a bi-directional interface. Together, they provide a unified solution for managing, securing, and monitoring AI deployments across an organization. This integration facilitates comprehensive AI governance by combining security insights with governance workflows.

    Key Integration Features:

    • Discovery and Inventory Management: Guardium AI Security automatically discovers AI models, data, and applications across various environments, including identifying unregistered or "shadow AI" deployments. These findings are shared with watsonx.governance to enrich its AI inventory, ensuring all AI assets are accounted for and governed appropriately. 

    • Security Posture Insights: The integration allows for the detection of security vulnerabilities and misconfigurations within AI deployments.

    • Unified Risk and Compliance Dashboard: By sharing underlying data, the integration provides a consolidated view of security risks and compliance status. This unified dashboard supports cross-functional collaboration among security, risk, and compliance teams, streamlining the management of AI-related risks.

    • Automated Governance Workflows: The combined capabilities facilitate automated workflows for AI governance, including risk assessments, compliance checks, and policy enforcement, thereby accelerating the deployment of secure and compliant AI solutions.

    This integration is particularly beneficial for organizations aiming to scale their AI initiatives while maintaining robust security and compliance standards.​

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