GenAI is no longer an R&D side project; it now answers tickets, writes marketing copy, even ships code. That shift exposes organisations to new failure modes — model poisoning, prompt injection, catastrophic hallucination — that sit outside traditional security audits. Meanwhile, lawmakers are racing to codify “trustworthy AI”:

EU AI Act entered into force 1 Aug 2024; general-purpose AI obligations start 2 Aug 2025, full high-risk rules by 2027.
The U.S. revoked EO 14110 but agencies continue issuing sector guidance, and a fresh Jan 2025 order prioritises “American AI leadership” while scrapping legacy red tape.
ISO/IEC 42001 launched the first certifiable AI-management standard; Anthropic’s Jan 2025 certification made the playbook public.

C-suites now face a dual mandate: keep pace with fast-evolving regulation and prove the security of opaque, learning systems.

This guide shows CISOs how to bolt AI-specific controls onto familiar pillars — ISO 27001, NIST CSF, SOC 2 — so you can ship LLM features and pass the inevitable audit.

Unique Security and Compliance Risks of LLMs

AI-Specific Threat	Why It Matters	Example Controls
Training-data poisoning	Backdoors change model behaviour post-deployment	Signed datasets, checksums, supplier due-diligence
Prompt injection / jailbreaks	Users override guard-rails; disallowed output leaks	Input sanitisation, constrained sandboxes, auto-moderation
Sensitive-data memorisation	Model regurgitates secrets; GDPR or IP breach	Data minimisation, differential privacy, output scanning
Hallucination & factual drift	Wrong answers cause financial or legal harm	Human-in-the-loop review, retrieval-augmented generation
ML supply-chain attacks	Compromised weights / PyPI packages insert malware	SBOMs for models & code, reproducible builds
Jurisdictional data moves	Cloud training crosses borders; violates residency laws	Region pinning, encryption at rest & in transit

These risks are unique to AI/LLMs or amplified by AI, and they require mapping to security controls. We next evaluate how existing security frameworks (ISO 27001, NIST CSF, etc.) cover such issues, and where gaps exist.

Actionable tip:Embed AI-specific risks into the enterprise threat model and treat poisoned data or jailbreaks exactly like zero-day vulns — track them in your risk register with owners and SLAs.

How Classic Frameworks Map to LLM Risks

Traditional infosec standards still form the bedrock of any AI security programme. They give you policies, audits, and muscle memory that every regulator recognises — but none were written with prompt-injection or fine-tune poisoning in mind. Below is the at-a-glance coverage map, followed by a concise look at what each framework adds (and misses) when LLMs enter the stack.

Framework	What It Covers Well	Where You’ll Still Have Gaps
ISO 27001	Asset inventory, role-based access, incident response	Model-integrity testing, bias/hallucination metrics
NIST CSF	Identify–Protect–Detect–Respond–Recover lifecycle	Translation to concrete AI controls; ethics / safety
SOC 2	Proof of operational rigour, data-handling controls	Prompt-injection resilience, content accuracy
CIS Controls v8	Hardening infra, patching, logging, IR drills	Dataset provenance, red-teaming models
CCSS	Key custody & transaction integrity (for crypto apps)	Broader ML risks; prompt security; supply-chain tampering

ISO/IEC 27001: Information Security Management

ISO/IEC 27001 is the leading globally recognized standard for information security management systems (ISMS). It provides a systematic, risk-based approach to protecting sensitive information across an organization. Achieving ISO 27001 certification means a company has established policies and controls covering areas like access control, physical security, supplier security, incident response, and more.

For an AI-focused organization, ISO 27001 creates a strong foundation of general security hygiene. Many controls in ISO 27001’s Annex A can be directly applied to AI operations. For example:

Asset Management: Inventorying information assets would include training datasets, model files, and research code — ensuring the organization knows what critical AI assets exist (addressing part of CIS Control 1 as well).
Access Control: ISO 27001 mandates role-based access and least privilege, which helps prevent unauthorized access to model training environments or model artifacts (mitigating risks of model tampering by insiders or external actors).
Supplier Security: Requirements to assess and secure third-party suppliers align with AI supply chain risk management — e.g. vetting cloud platforms or external pretrained models used in development.
Cryptography and Key Management: If LLM services involve API keys or encryption of model weights, ISO 27001’s cryptography controls ensure proper handling of those secrets.
Incident Response: An ISMS must include incident handling processes, which an AI company can extend to include responding to model exploits (like detecting and responding to prompt injection incidents or data leaks).

However, ISO 27001 is technology-neutral. It does not explicitly mention machine learning, model integrity, or data poisoning. So while it enforces a culture of security and many relevant controls, it doesn’t provide specific guidance on AI risks. A company with ISO 27001 might still need additional procedures to handle issues like hallucination monitoring or bias evaluation, which are outside ISO 27001’s traditional scope.

In summary, ISO 27001 is a crucial part of the puzzle — establishing trust through a certified security program — but it must be augmented with AI-specific risk management.

NIST Cybersecurity Framework (CSF)

The NIST Cybersecurity Framework (CSF) is a widely adopted voluntary framework for improving cybersecurity risk management, originally designed for critical infrastructure. It provides a set of core functions — Identify, Protect, Detect, Respond, Recover — and a catalog of high-level outcomes and controls. Organizations use the NIST CSF as a flexible blueprint to assess and strengthen their security posture.

For an LLM-building organization, NIST CSF offers a structured approach to cover the basics:

Identify: Catalog AI assets (data, models, computing resources) and identify business contexts (e.g., how an LLM service ties into operations) — similar to ISO’s asset management but framed as a risk identification step.
Protect: Implement safeguards for AI systems (access controls, data security, maintenance processes) — e.g., controlling access to training data, hardening ML development environments, and segmenting networks used for model training to prevent unauthorized access or exfiltration.
Detect: Establish monitoring to promptly detect cybersecurity events — in an AI context, this could mean monitoring for unusual model outputs (potential sign of compromise or poisoning) or detecting suspicious access patterns to the model or data (as one would detect any intrusion).
Respond and Recover: Develop incident response plans that include AI incidents (like responding to a discovered backdoor in a model or a malicious prompt causing system misuse). Recovery might involve retraining models from backups if a compromise is detected.

The NIST CSF does not list “prompt injection” or “model poisoning” by name, but its broad categories can be interpreted to include them. For example, “Detect” could encompass detecting anomalous model behavior, and “Protect” could cover securing training processes to prevent poisoning. In practice, an AI company would map specific AI risks to the CSF categories. NIST CSF is valuable as a high-level checklist to ensure no major security function is neglected.

That said, CSF is high-level and framework-agnostic. It requires translation into concrete controls for AI. It also doesn’t address AI ethics or model quality. Therefore, while NIST CSF ensures you have a comprehensive cybersecurity program on paper, it won’t by itself guarantee that model-specific vulnerabilities are fully mitigated — those details depend on how thoroughly the organization implements relevant controls under the CSF’s umbrella.

SOC 2 (System and Organization Controls 2)

SOC 2 is an audit and reporting framework, defined by the AICPA, that evaluates an organization’s controls against the Trust Services Criteria — Security, Availability, Processing Integrity, Confidentiality, and Privacy. In the context of AI service providers (like API platforms offering an LLM), SOC 2 Type II compliance is often expected by enterprise clients as proof of strong operational security and data handling practices.

For example, OpenAI has undergone SOC 2 Type II audits for Security and Confidentiality principles on its ChatGPT Enterprise and API offerings. This indicates to customers that OpenAI’s internal processes protect customer data and keep the service secure and reliably available. Key aspects relevant to LLM companies include:

Security: Controls that protect against unauthorized access (this overlaps with ISO 27001 and NIST CSF controls — e.g., access management, network security). For an LLM, that means ensuring the model endpoints, training servers, and data repositories are secured from breaches.
Confidentiality: Measures to protect sensitive data from unauthorized disclosure. An LLM company handling customer prompts or fine-tuning on client data must demonstrate encryption, need-to-know access, and data retention policies to meet this criterion.
Processing Integrity: Ensuring system processing is complete, valid, accurate, and authorized. In an AI context, this could relate to how input is handled and outputs are delivered without tampering — though guaranteeing output accuracy is tricky (hallucinations are a known issue), the focus is more on system reliability and lack of unauthorized alteration of responses.
Availability: Ensuring the AI service is reliably accessible. For instance, having redundancy for model inference servers and plans to handle outages.
Privacy: If the AI system deals with personal data, demonstrating compliance with privacy requirements (though often privacy is handled via separate certifications like ISO 27701 or GDPR compliance efforts).

SOC 2 audits force organizations to formalize and document their controls. However, like ISO 27001, SOC 2 is general — there is no SOC 2 control that specifically says “check for model poisoning.” It is up to the organization and auditor to consider AI risks under the broader criteria. For example, a company might include an AI model review in change management controls (security) or have a process to review training data for sensitive info (confidentiality).

In summary, SOC 2 is about building customer trust — a SOC 2 report signals your LLM service has mature security and internal controls. It covers the basics of security and data protection (which certainly helps mitigate some AI risks like data leakage), but it doesn’t guarantee coverage of all AI-specific pitfalls. Still, any LLM company offering services should strongly consider SOC 2 to meet market expectations and as a baseline for security maturity.

CIS Critical Security Controls (CIS Controls)

The CIS Critical Security Controls (currently 18 in version 8) are a prescriptive set of best practices developed by the Center for Internet Security. They are prioritized and simplified recommendations that organizations can implement to strengthen cybersecurity. Many companies adopt CIS Controls as a practical to-do list for IT security, often aligning with or supplementing frameworks like NIST CSF.

Applying CIS Controls to an AI/LLM environment provides very concrete benefits:

Inventory and Control of Assets (CIS Control 1 & 2): Ensure you have an inventory of all computing devices, cloud instances, and software in your AI development environment. For AI, this means tracking data sources, model files, and ML code repositories as well. Unmanaged assets could be entry points for attackers to plant a poisoned dataset or exfiltrate model weights.
Secure Configuration (Control 4): Mandate hardened configurations for servers and cloud instances used in model training and deployment. This reduces vulnerabilities that could be exploited to gain access for malicious manipulation.
Account Management and Access Control (Controls 5 & 6): Strictly manage identities and privileges in the AI team. This aligns with least privilege principles to prevent an intern or compromised account from altering critical training data or models (guarding against internal threats to model integrity).
Continuous Vulnerability Management (Control 7): Regularly scan and patch the software libraries and frameworks used in model development (e.g. PyTorch, TensorFlow). Many ML projects pull code from open-source; this control helps address supply chain risks by ensuring known vulnerabilities in those components are patched.
Monitoring and Logging (Controls 8 and 14): Logging access to training datasets and model files, and monitoring for anomalies (e.g., large data downloads, or unusual model outputs). This ties directly into detecting attempts at data theft or signs of model misuse.
Incident Response (Control 17): Have an incident response plan that explicitly covers AI incidents. If a model is exhibiting odd behavior (potential poisoning) or if a data breach exposes training data, the team knows how to respond swiftly.

CIS Controls are quite technical and granular, which makes them very useful for DevOps and security engineers in an AI company. They ensure fundamental security measures are not overlooked. Implementing CIS Controls would inherently cover some AI risks (for instance, proper data protection and access controls will mitigate data exposure and unauthorized model access).

Yet, CIS Controls are not infallible for AI-specific issues: they might not tell you how to handle a model hallucination harming your brand or how to ensure fairness in AI outputs. They are focused on classic cybersecurity (keeping systems hardened and intruders out). In combination with an AI risk management approach, however, CIS Controls form a necessary baseline to make sure the infrastructure around your AI is secure. An LLM model might be a cutting-edge asset, but it still runs on servers and code that need basic cyber protections.

Where Traditional Frameworks Fall Short for AI

While implementing existing frameworks is necessary, it often isn’t sufficient to address the full spectrum of AI/LLM risks. Some notable gaps include:

Lack of AI-Specific Threat Modeling: Traditional security standards don’t explicitly recognize threats like adversarial ML attacks. For example, ISO 27001 requires risk assessment, but unless the assessors are knowledgeable about AI, they might not consider data poisoning of a training set as a top risk. The frameworks leave it to practitioners to insert these new threat scenarios. Many organizations without AI security expertise may simply miss risks like model inversion attacks (extracting training data from a model) because they are not on the typical checklist.
Data Quality and Integrity Checks: Frameworks like CIS or ISO focus on protecting data from theft or unauthorized change, but they don’t ensure that training data itself is valid and free from subtle manipulation. A model could be trained on biased or poisoned data without tripping any ISO control if the data was not “unauthorized” per se. This is a gap — ensuring the integrity of training data and provenance might require new controls (e.g., rigorous dataset curation, checksums for training data, or only using high-quality trusted data sources).
Model Behavior and Output Risks: Traditional infosec controls protect the systems around the model, but not the model’s behavior. An AI can be secure from external hackers yet still produce harmful or nonsensical output due to inherent flaws. Frameworks don’t cover issues like hallucination, toxicity, or bias in AI outputs because those aren’t classic security properties (they fall under reliability, ethics, safety). This is a major gap for compliance — e.g., if a finance chatbot LLM gives wrong tax advice, ISO 27001 has nothing to say about that. New frameworks (as we’ll see with the AI Act or AI ethics principles) are starting to demand oversight of model behavior, not just IT security.
Emergent and Dynamic Risks: AI systems can evolve (models get updated or learn continuously from new data). Traditional frameworks assume relatively static systems where updates are controlled via change management. An LLM that learns from user interactions could drift into unsafe behaviors without a clear “incident” triggering it. Our existing frameworks don’t describe how to govern an ongoing learning process. Continuous evaluation of model performance and risk is something new that isn’t captured by periodic audits like SOC 2.
Cross-Disciplinary Nature: AI risk spans technical security, but also legal compliance (data protection laws), ethical considerations (bias/fairness), and even safety (if AI could affect physical systems or human decisions). Traditional security frameworks primarily focus on confidentiality, integrity, availability (the CIA triad) of information. They don’t explicitly address regulatory classifications like the EU’s AI Act risk levels or ethical principles like those from OECD. Thus, a purely ISO/NIST-based approach might leave an organization ill-prepared for an audit by a regulator asking “How do you ensure your AI doesn’t discriminate or infringe on privacy beyond just cybersecurity?”.
Metrics and Testing: There’s also a gap in testing methodologies. In cybersecurity, one can do penetration testing or vulnerability scanning guided by frameworks. For AI, one might need red-teaming of the model (trying to prompt it into bad behavior) or evaluating fairness metrics. These activities are not standard practice in general IT security audits. Leading AI firms have had to create their own testing regimes (e.g., OpenAI’s red team and “Preparedness” evaluations) because none of the traditional frameworks tell you to do an adversarial evaluation of model behavior under stress.

In essence, organizations solely relying on traditional frameworks might achieve a false sense of security — they may pass an SOC 2 audit yet have unaddressed ML-specific vulnerabilities. To bridge this gap, new AI-focused frameworks and regulations have been emerging, which we’ll explore next. These are designed to explicitly tackle the unique risks and ethical considerations of AI, and they provide guidance that can augment an organization’s compliance strategy.

Before moving on, it’s worth noting that the security community is acknowledging these gaps. For example, the SANS Institute recently outlined that prompt injection and model poisoning require new controls and governance beyond typical cybersecurity measures. This reinforces that to be truly secure and compliant, AI organizations must look beyond legacy frameworks and incorporate AI-specific risk management.

Emerging AI-Specific Frameworks & Regulations

LLM security has outgrown classic infosec playbooks. Five new references now set the global baseline for “trustworthy AI.” They fill the blind spots — model behaviour, training-data quality, cross-border risk — that ISO 27001 or SOC 2 never addressed.

Framework/Regulation	Why It Matters for LLMs	What CISOs Should Do
EU AI Act	First binding, risk-tiered AI law; GPAI and “frontier” models get special duties.	Build a tech file (data lineage, red-team logs), prepare for CE-mark audits.
U.S. agency/industry playbook	Sector guidance + voluntary big-tech commitments set de-facto rules.	Align controls to NIST AI RMF; publish model cards, watermark outputs.
NIST AI RMF 1.0	Lifecycle framework — Govern → Map → Measure → Manage — with GenAI profile.	Create an internal RMF profile and risk register for prompt-injection, hallucination, bias.
OECD AI Principles	Global ethical compass echoed by EU & G20.	Reference in policies; show fairness / transparency metrics in customer docs.
ISO/IEC 42001	First certifiable AI-management system; pairs with ISO 27001.	Extend ISMS to 42001; plan certification to reassure clients and regulators.

EU AI Act — A Comprehensive AI Regulation

Perhaps the most ambitious effort is the European Union’s AI Act, set to be the world’s first comprehensive law regulating AI systems. The AI Act takes a risk-based approach: it classifies AI systems into risk categories (unacceptable risk, high-risk, limited risk, minimal risk) with corresponding requirements for each. Key points relevant to LLM developers include:

High-Risk AI Systems: AI used in sensitive areas (like credit scoring, employment decisions, medical devices) will be classified as high-risk and must meet strict requirements (risk assessment, documentation, human oversight, etc.) before deployment. If an LLM is part of such a system, the provider must implement an AI quality management system and undergo conformity assessments (similar to audits) to legally offer it in the EU.
General-Purpose AI (GPAI) / Foundation Models: Recognizing the rise of foundation models (like GPT-4, Claude, etc.), the AI Act has specific provisions for general-purpose AI models — essentially large models trained for broad tasks. Providers of these models (often big AI labs) will have obligations even if their model is not itself a high-risk end-use system. For example, they must publish extensive technical documentation, disclose the training data sources (a “training data summary”), and ensure robust risk management.
Systemic Risk “Frontier” Models: The Act introduces the concept of “general-purpose AI models with systemic risk” — meaning the most advanced, powerful models that could pose large-scale harms (examples given include models that might help develop biological weapons or that are so autonomous they raise control issues). Developers of such frontier models will face additional requirements: they must notify the EU Commission about these models, conduct thorough risk assessments and red-team testing, mitigate identified risks, report any serious incidents or misuse, and ensure “adequate cybersecurity” for these models. In short, companies at the cutting edge must act responsibly and share information with regulators.
Transparency and User Rights: The AI Act will likely require that AI systems disclose when users are interacting with AI (to prevent deception) and, for generative AI, that outputs are indicated as AI-generated (e.g., watermarking deepfakes). LLM providers might need to build in watermarking or metadata to comply.
Data Governance: There are requirements around training data — it should be checked for biases and errors where feasible, and the act forbids using data in ways that violate EU privacy and copyright laws. This forces LLM developers to be careful about scraping content (respecting IP) and personal data inclusion (GDPR compliance).
Conformity Assessment and CE Marking: For high-risk AI (and possibly some foundation models), providers will need to go through assessments (potentially by external notified bodies) to get a CE marking (certificate of conformity) before deploying in the EU. This is akin to how medical devices are regulated. It means formal audits of the AI development process, documentation, and testing outcomes.

Overall, the EU AI Act is pushing organizations to have comprehensive AI risk management and governance. A company building LLMs intended for the EU market should start aligning with these requirements now. For example, they should begin producing the technical documentation that the Act will mandate (detailing model architectures, training methods, etc.), and implementing mechanisms for systematic risk assessment and mitigation for their models. The Act explicitly calls for measures like ensuring cybersecurity of AI systems and reporting incidents, which maps to having robust MLOps security and incident response for things like discovered model vulnerabilities.

The EU AI Act is expected to be enforced in the next 1-2 years (it’s in final legislative negotiations as of 2025). It will be a legal requirement, not just guidance. Therefore, LLM companies aiming to be “AI Act ready” are taking steps such as: appointing AI compliance officers, documenting their training datasets and known limitations, doing preliminary audits of biases in their models, and watching the final text closely. Compliance will be an ongoing process, much like maintaining ISO certification, but with a specific focus on trustworthy AI.

U.S. AI Initiatives — Executive Order and Beyond

In the United States, a different approach is emerging — a combination of executive actions, agency guidance, and voluntary commitments by industry, in lieu of comprehensive legislation (at least for now). A landmark move was the U.S. Executive Order on Safe, Secure, and Trustworthy AI (Executive Order 14110, issued October 30, 2023). This EO (while recently revoked with the administration change in 2025) signaled federal priorities and kicked off several initiatives:

Red-Teaming and Reporting: The EO required that developers of the most powerful AI models (foundation models with potential dual-use or security implications) share the results of safety tests (red-team evaluations) with the U.S. government. This was a move to ensure transparency about frontier model risks — effectively, companies must do adversarial testing and cannot keep the findings entirely private if the models are high consequence.
NIST AI Safety Standards: It directed NIST to develop guidelines, standards, and best practices for AI safety and security. NIST was already working on the AI Risk Management Framework, but the EO accelerated efforts to create more specific standards (for example, standards for biometric AI or for testing AI for dangerous capabilities).
Critical Infrastructure Guidance: Federal agencies were tasked to assess how AI could affect critical infrastructure security and to issue best practices for managing AI-related cyber risks in sectors like finance and healthcare. This means organizations in those sectors will likely see regulators (like banking regulators) echo these best practices — e.g., requiring banks that deploy AI models for credit decisions to follow robust security and bias mitigation processes.
Watermarking and Content Authentication: The EO mentioned the development of methods for watermarking AI-generated content and authenticity verification. This is more targeted at AI used to create media (deepfakes, etc.), but could impact LLMs that generate text — we may see guidelines to tag AI-generated text/data to prevent misinformation.
IaaS Reporting of Compute Usage: Interestingly for AI developers using cloud compute, the EO sought to apply the Defense Production Act to require cloud providers (IaaS) to report when foreign actors order large amounts of compute (to detect potential AI model training for weapons). This highlights national security concerns around who is training large models.
AI Security and Safety Board: The EO proposed establishing an AI Safety and Security Board, analogous to the Cyber Safety Review Board, to investigate major AI incidents and advise on future safeguards. This indicates future post-incident investigations and knowledge sharing on AI incidents, which companies will need to cooperate with.

Although Executive Orders can be changed with administrations, the private sector has also been proactive. In July 2023, even before the EO, the White House announced Voluntary AI Commitments by leading AI companies (OpenAI, Google, Meta, Anthropic, etc.), which include:

Conducting internal and external security testing (red teaming) of AI models before release.
Sharing information on AI vulnerabilities and best practices across the industry and with governments.
Developing techniques like watermarking for AI-generated content to identify misuse.
Publicly reporting model capabilities, limitations, and use guidelines (e.g., model cards or system cards).
Prioritizing research on societal risks of AI (like bias, misinformation, cybersecurity misuse).

These commitments align with the EO and set a de facto expectation. An LLM firm that wants to be seen as a responsible player should implement these practices. In fact, OpenAI has published a “Preparedness Framework” and safety policies and noted it won’t release models above a certain risk threshold until mitigations are in place. Anthropic has its “Responsible Scaling Policy” focusing on gradually increasing model capabilities with proper safeguards. Even if not enforced by law, these practices are becoming industry norms.

Looking forward in the U.S., we anticipate sector-specific guidelines: for instance, the FDA might issue guidance for AI in medical devices, the FTC is closely watching AI for consumer protection issues (and has warned about misleading claims or biased algorithms). Unlike the EU, the U.S. might not have one AI law, but companies will need to comply with multiple narrower regulations (e.g., data privacy laws like CCPA, or EEOC rules if AI is used in hiring) and show adherence to frameworks like NIST’s.

Staying agile is key. A recommendation is to follow NIST’s work (they launched an AI Risk Management Center to foster adoption of the AI RMF and track best practices) and to engage in industry groups or standards efforts. By aligning early with the principles of the EO and the voluntary commitments — essentially treating them as if they were mandatory — AI companies can be well-positioned even as the regulatory landscape shifts.

NIST AI Risk Management Framework (AI RMF)

In January 2023, the U.S. National Institute of Standards and Technology released the AI Risk Management Framework (AI RMF) 1.0, a landmark guidance for AI governance. Unlike the NIST Cybersecurity Framework which is broadly about IT security, the NIST AI RMF is specifically tailored to AI systems. It is voluntary but has quickly become an influential reference for AI developers and policymakers worldwide.

The NIST AI RMF’s goal is to help organizations manage the many risks of AI in a comprehensive, systematic way. It focuses on the concept of AI trustworthiness — ensuring AI systems are valid, reliable, safe, secure, resilient, explainable, privacy-enhanced, and fair. In NIST’s words, it aims to help incorporate “trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems”.

Key components of AI RMF:

Core Functions: Similar to the Cybersecurity Framework, the AI RMF defines functions: Govern (overarching AI governance), Map (contextualize AI risks), Measure (analyze and assess risks), and Manage (mitigate and monitor risks). This is a lifecycle view where an organization first lays governance foundations, then identifies risks (Map), evaluates them (Measure), and takes actions (Manage) — all in a continuous improvement loop.
Categories and Sub-categories: Under these functions, it provides specific categories. For example, under “Govern” it includes fostering a risk culture and setting roles and responsibilities for AI risk; under “Map” it includes understanding the AI system’s context, benefits, and potential harms; under “Measure” are categories like validation and performance measurement, and under “Manage” are categories like risk mitigation techniques and incident response planning for AI.
Guidance Examples: The framework document gives examples of outcomes or actions. For instance, one outcome is that organizations regularly monitor models in operation for anomalies or performance drifts (this directly addresses issues like an LLM starting to behave unexpectedly). Another example: organizations assess and document training data quality, including representativeness and biases, which helps tackle bias and poisoning risks.

The AI RMF is technology-neutral but explicitly about AI; it doesn’t prescribe one way to achieve things but provides a vocabulary and structure. For instance, it doesn’t say “do X to prevent prompt injection,” but it will urge you to identify potential misuse vectors and have mitigations (which could include prompt filtering, user access control, etc., as specific measures).

One useful aspect is that NIST released a Profiles — for example, a Generative AI Profile in July 2024 — which tailors the general AI RMF to generative models like LLMs. This profile helps organizations pinpoint the unique risks of generative AI (like hallucination, content moderation issues) and suggests actions to manage them. NIST also provides a Crosswalk mapping the AI RMF to other frameworks, showing how it can complement ISO 27001, the OECD principles, etc.

Though voluntary, the AI RMF has been referenced in policy (the U.S. EO mentioned above calls for use of AI RMF in certain sectors). Some enterprises are adopting it internally to structure their AI governance. Importantly, it’s not a certification — you don’t get “NIST AI RMF certified” — but you might get asked by business partners or regulators “Are you following the NIST AI RMF?” similar to how one might ask about ISO or NIST CSF.

For an LLM-building company, adopting NIST AI RMF means:

Establishing an AI risk governance committee or role (to satisfy “Govern”) that oversees policies for AI development (e.g., responsible AI principles, tying into company values).
During development, performing risk mapping exercises: identify potential failure modes of your model (data leakage, misuse, bias, etc.), the impacted stakeholders, and legal/ethical implications (“Map” function).
Measuring and testing: implementing metrics or tests for those risks — e.g., measuring how often the LLM produces disallowed content, testing its robustness to adversarial prompts (“Measure”).
Managing risks: taking actions like refining training data, adding guardrails, monitoring in production, and having mitigation plans if something goes wrong (“Manage”).

NIST’s framework is quite comprehensive. By following it, a company can be confident it’s ticking off a lot of boxes that regulators or auditors might inquire about, even if informally. It also aligns with other guidelines: for example, AI RMF echoes many OECD AI Principles (like transparency, accountability) and will help in meeting obligations of the EU AI Act (like risk assessment, documentation, continuous monitoring).

OECD AI Principles

The OECD AI Principles (adopted in 2019, updated in 2024) represent an international consensus on AI governance values. They were agreed upon by dozens of countries (including the US, most of Europe, and others) and have also influenced the G20’s AI guidelines. While high-level and not legally binding, they carry weight as a normative framework for trustworthy AI.

There are five fundamental value-based principles for AI per OECD:

Inclusive growth, sustainable development and well-being: AI should benefit people and the planet, driving social good and innovation responsibly.
Human-centered values and fairness: AI should respect human rights, freedom, dignity, and be designed to be fair (avoid unfair bias).
Transparency and explainability: There should be transparency around AI systems — people should know when they are interacting with AI, and AI decisions/outcomes should be explainable to a level commensurate with context.
Robustness, security and safety: AI systems must be robust and safe throughout their lifecycles, with appropriate safeguards to function as intended and minimize risks. This includes resilience to attacks — essentially an early call-out of AI security specifically — and to unreliable results.
Accountability: Those deploying AI systems should be accountable for their proper functioning in line with the above principles.

Additionally, the OECD principles provide five recommendations for governments, like investing in AI research, fostering a digital ecosystem, shaping policy, etc., which are less directly relevant for a company’s internal compliance.

For an LLM company, the OECD principles can serve as a guiding star for its AI policy:

They encapsulate why security and safety are paramount (principle 4 specifically emphasizes security of AI, which validates a focus on things like preventing model misuse).
They introduce ethical dimensions like fairness and transparency which typical security frameworks ignore. So a company might adopt internal policies (or AI ethics charters) saying, e.g., “We will conduct bias audits on models and be transparent about their capabilities, aligning with OECD AI Principles.” This can be a selling point to clients and regulators that the company is following globally recognized best practices.
The principles also mention risk assessment implicitly — you can’t ensure robustness or fairness without examining where things could go wrong.

While one doesn’t get “certified” on OECD principles, they are often referenced in other frameworks and laws. The EU AI Act’s ethos, for example, is consistent with OECD (the Act seeks to ensure safety, transparency, etc.). If you align with OECD, you are likely in good shape philosophically for complying with regional regulations too.

In marketing to enterprise clients, an AI firm might even explicitly state alignment with these principles to build trust. For instance: “Our AI development process follows the OECD’s AI Principles to ensure our models are fair, transparent, and secure by design.” This establishes credibility to an audience that might not know the deep tech, but recognizes international standards.

ISO/IEC 42001 — AI Management System Standard

Just as the ISO 9001 standard exists for quality management and ISO 27001 for security management, in late 2023 ISO introduced ISO/IEC 42001:2023, the first AI-specific management system standard. This is a major development: ISO 42001 provides requirements and guidance for establishing an AI Management System (AIMS) within an organization. In simpler terms, it tells companies how to put in place policies, processes, and controls to manage AI responsibly and mitigate risks.

Key aspects of ISO 42001:

It covers AI governance structure, requiring organizations to define roles and responsibilities for AI oversight (similar to how ISO 27001 requires a security organization and leadership commitment).
It mandates AI risk management processes, including performing AI system impact assessments (how could the AI affect stakeholders or cause harm) and integrating those into development.
It requires consideration of ethics and accountability (e.g., ensuring human oversight where appropriate, addressing fairness).
It also emphasizes AI lifecycle management, meaning controls at each stage — data preparation, model training, verification/validation, deployment, monitoring, and end-of-life of models.
Third-party and supply chain: If you rely on third-party AI components or services, ISO 42001 says you must manage those relationships to ensure they meet your AI governance standards as well.

In essence, ISO 42001 is trying to standardize what a “responsible AI program” within a company looks like. It’s quite comprehensive, and importantly, certifiable — an organization can be audited and certified against ISO 42001, similar to ISO 27001. In fact, some companies have already started. For example, Anthropic (an AI lab) was reported to have obtained certification to ISO/IEC 42001:2023 in addition to ISO 27001. This signals that leading AI firms are embracing formal certification to demonstrate their AI governance maturity.

One cool thing is that ISO 42001 is designed to complement existing laws — the standard explicitly mentions alignment with regulatory requirements like the EU AI Act. So if you implement ISO 42001, you’re likely satisfying much of what the AI Act will require (risk assessments, documentation, etc.) and you have a defensible system if regulators inquire. KPMG noted that ISO 42001 will be a “cornerstone” providing essential guidance for compliance with emerging AI laws.

For an LLM company, pursuing ISO 42001 certification could soon become as important as ISO 27001:

It gives a structured framework to operationalize AI ethics and risk management. Instead of ad-hoc measures, you’d have an integrated management system with continuous improvement (the PDCA cycle applied to AI processes).
It reassures enterprise clients and partners that you aren’t just talking about AI ethics/safety — you have a vetted program in place. This can be a market differentiator.
It prepares you for AI audits. We foresee that just like security audits are routine, AI-focused audits (for bias, safety, etc.) will become common. ISO 42001 provides the auditable criteria for that.
The standard covers AI-specific “controls” in a broad sense: e.g., it might require documented procedures for data anonymization if using personal data for training, or having a mechanism to handle user feedback about incorrect AI outputs. These fill the exact gaps we identified in older frameworks.

Implementing ISO 42001 will involve cross-functional effort (security, legal, engineering, HR for training, etc.), as it touches policy and technical measures. It’s quite new, so best practices in implementation are still forming, but aligning with it early can put a company ahead in the compliance curve.

Industry Best Practices and Guidelines (OpenAI, Anthropic, Google DeepMind)

Beyond formal standards and laws, much can be learned from the guidelines published by leading AI organizations themselves. Companies like OpenAI, Anthropic, and Google DeepMind have been vocal about AI safety and have released frameworks or policies that others can emulate:

OpenAI has shared a set of “AI safety practices” it follows internally. These include:

Empirical model red-teaming and testing before release: OpenAI conducts extensive internal testing and invites external experts to “red team” their models (GPT-4, for example, had over 50 experts probing it for misuse). They have a risk threshold — if a new model is deemed too risky (e.g., could be used to generate dangerous information), they delay release until mitigations reduce the risk level.
Continuous monitoring for abuse: After deployment, OpenAI uses automated systems and human reviewers to monitor how the model is used via the API, detecting misuse patterns (like attempts to get disallowed content). They even shared that they and Microsoft detected state-sponsored actors abusing AI APIs and disclosed it.
Iterative alignment research: They invest in research to improve model alignment with human intentions — for example, studying how to make models more resistant to “jailbreak” prompts over time.
Preparedness & Governance: OpenAI developed an internal Preparedness Framework that guides evaluating and mitigating extreme risks (like biosecurity threats from AI). They also have engaged with policymakers and follow voluntary commitments (like those we discussed).

Insight: Having an internal playbook for evaluating catastrophic risks (even if they seem unlikely) is part of being a responsible LLM developer.

Anthropic introduced the concept of Constitutional AI to align AI behavior with a set of principles. They also published a Responsible Scaling Policy detailing how they will manage the advancement of their models safely. Key points include:

Phased deployment with increasing model capability only after meeting safety criteria at each phase.
Tiered access levels (more powerful models or less filtered models only given to vetted partners with proper use cases).
Real-time and post-hoc monitoring systems specifically to catch new forms of misuse (including advanced prompt classifiers to detect attempts to bypass safeguards).
Incident response specifically for things like new discovered jailbreaks, including the ability to quickly patch models or adjust prompts to re-enable safety if a breach is found.

Insight: Even outside formal standards, LLM companies should set internal “pacing” rules for model development, ensuring that as models get more capable, proportional investments in safety and oversight are made. Also, advanced AI-driven monitoring (using AI to watch AI, like Anthropic’s layered classifier approach) is emerging as a best practice.

Google (and DeepMind) have published AI Principles (back in 2018 Google set forth principles such as being socially beneficial, avoiding unfair bias, being accountable to people, etc.). They’ve also open-sourced toolkits for AI fairness and model interpretability. Google has a deep focus on ML supply chain security in their MLOps, releasing frameworks like TensorFlow Secure Supply Chain. They also actively contribute to standards (Google Cloud, for instance, is aligned with ISO 42001 as noted by their compliance pages).

DeepMind has an Ethics & Society unit that produced reports on responsible AI, and they pioneered red-teaming for AI alignment in systems like Sparrow.

Insight: Integrating ethical review and red-teaming early in model design is key. Also, secure your ML pipeline from data to deployment — Google’s practices around integrity (e.g., model cards that document how a model was trained and tested) help with traceability and accountability.

All these companies stress collaborating with external researchers and being more open about AI system limitations. Publishing a model card or system card for major models (as done for GPT-4, Claude 2, etc.) that details the model’s intended use, limitations, and evaluation results is becoming an industry standard. This transparency is both a trust-building measure and may soon be a compliance requirement (the EU Act will essentially make technical documentation mandatory).

Finally, these leaders often go beyond compliance: they are trying to anticipate where future regulations might head (e.g., possibly requiring licensing of the most powerful models) and self-regulate accordingly.

An LLM startup or any company following in their footsteps should leverage the learnings from these pioneers — adopting similar safety policies, using their open research, and perhaps even using their models via APIs, which come with built-in safety features instead of reinventing the wheel.

Compliance Roadmap for LLM Organizations

Below is a “start-to-audit” plan that layers classic security frameworks with AI-specific controls.

#	Step	What You’re Aiming For
1	Govern	Permanent, board-visible AI oversight
2	Gap-map	Every LLM threat tied to an existing or new control
3	Add AI standards	NIST AI RMF profile and ISO 42001 alignment
4	Lock the pipeline	End-to-end MLOps security & red-team loops
5	Prove it	ISO 27001, SOC 2, plus voluntary AI audits
6	Stay current	Continuous evidence, regs watch, staff up-skilling

1. Establish an AI Governance Program

Integrate with ISO 27001: for example, extend your ISO scope to cover datasets, model weights, research code.
Assign ownership: appoint an AI Compliance Officer / steering committee.
Set policies: e.g., “no training data enters unless it clears bias & quality gates.”
Standards match-up: NIST AI RMF “Govern” + ISO 42001 org-structure clauses.

2. Map AI Risks to Controls (Gap Assessment)

List LLM-specific threats: poisoning, jailbreaks, hallucination, bias, data leakage, etc.
For each, map to an existing CIS v8 / NIST CSF control; flag any gaps.
Where gaps exist, create new controls (e.g., prompt-sanitisation sandbox).
Record everything in a risk register or compliance matrix for audit-trail clarity.

3. Augment with AI-Specific Frameworks

NIST AI RMF: build an internal profile covering training-data governance, model validation, transparency, human oversight.
ISO 42001: plan certification early; it dovetails with 27001 and satisfies EU AI Act expectations.
Use both frameworks as living checklists.

4. Implement Robust Technical & MLOps Controls

Data security: encrypt & permission datasets; anonymise personal data.
Environment hardening: isolated build clusters, container security, checksum verification.
Versioning / change control: peer-review every dataset & model update.
Monitoring & anomaly detection: system-level (GPU/net) + output-level (jailbreak spikes).
Pen-testing & AI red-team: quarterly exercises; feed findings into backlog.
Incident response: playbooks for disinformation, privacy leaks, retraining triggers.

5. Achieve Key Certifications & Audits

ISO 27001: include AI infra in scope.
SOC 2 Type II: Security + Confidentiality minimum for any SaaS/API offering.
Voluntary AI ethics / quality audits: bias, privacy, safety — early adoption builds trust and future-proofs compliance.

6. Continuous Compliance & Future-Proofing

Automate evidence: log data feeds both SecOps and audit artefacts.
Reg-watch: EU AI Act tech file, U.S. sector guidance, future ISO/IEEE drafts.
Join standards bodies: ISO SC42, IEEE, Partnership on AI.
Upskill: train engineers on OWASP LLM Top 10; certify compliance/legal teams in AI governance.
Transparency reports: publish model cards and annual safety updates — “show your work” to customers and regulators.

Conclusion

AI security isn’t a green-field discipline — it’s an overlay on proven infosec foundations. By following the tips outlined in this blog, you can create a layered compliance shield: the base layer being proven security frameworks (ISO 27001, SOC 2, CIS) to cover general cybersecurity and data protection; the next layer being AI-specific risk management (NIST AI RMF, ISO 42001) to cover model and algorithmic risks; and the outer layer being adherence to regulatory requirements (AI Act, etc.) and ethical principles.

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