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NIST’s New Cyber AI Profile: What It Means for Your Security Program and How to Get Involved

ByInnoVirtuoso

What if your SIEM could explain its alerts, your patching engine could prioritize vulnerabilities like a seasoned analyst, and your incident response runbooks could learn from every engagement? That’s the vision behind NIST’s newly announced effort to develop a “Cyber AI Profile” — and it could reshape how organizations design, deploy, and govern AI across the cybersecurity stack.

On February 14, 2025, the National Institute of Standards and Technology (NIST), through its National Cybersecurity Center of Excellence (NCCoE), invited industry, academia, and government to participate in building a Cyber AI Profile based on the NIST Cybersecurity Framework (CSF). The goal: create a practical, trustworthy playbook for using AI in defense — across threat detection, incident response, vulnerability management, and more — while mitigating risks like model poisoning, adversarial inputs, and privacy leakage.

If you’ve been testing AI-driven tools in your SOC, wrestling with explainability, or trying to map GenAI pilots to existing controls, this is your moment. Here’s what’s changing, why it matters, and how to get your team a seat at the table.

Link to the announcement: NIST: Participate in the development of a new Cyber AI Profile

What Is NIST’s Cyber AI Profile?

A “profile” in NIST CSF terms is a tailored implementation of the framework for a specific mission, sector, or outcome. The Cyber AI Profile will:

  • Map AI-enabled cybersecurity capabilities to the NIST CSF 2.0 Functions — Identify, Protect, Detect, Respond, and Recover — and their categories/subcategories.
  • Define controls, safeguards, and evaluation practices to make AI in cyber “trustworthy” (explainable, robust, and privacy-preserving).
  • Offer maturity guidance and roadmaps so organizations can move from pilots to repeatable, auditable AI operations in the SOC.
  • Provide reference architectures and interoperability guidance so teams can integrate AI across SIEM/XDR, EDR, SOAR, vulnerability management, identity, and more.

Think of it as the missing bridge between NIST’s AI risk guidance and the day-to-day tooling of modern cyber defense.

Helpful background: – NIST CSF 2.0: https://www.nist.gov/cyberframework – NIST AI Risk Management Framework (AI RMF 1.0): https://www.nist.gov/itl/ai-risk-management-framework – NCCoE projects and participation: https://www.nccoe.nist.gov/partners/how-participate

Why Now? AI-Driven Attacks Have Arrived — and So Have AI Defenses

AI is now a force multiplier — on both sides:

  • Attackers use automation to craft phishing, find exposed secrets, and adapt payloads faster than signatures can keep up.
  • Defenders apply ML to anomaly detection, user/entity analytics, automated triage, and predictive patching — but often without standardized governance or shared evaluation criteria.

NIST’s initiative aims to standardize the “how” of AI in cyber defense: not to pick winners among tools, but to codify trustworthy practices that raise the floor for everyone.

The Core Pillars: Explainability, Robustness, and Privacy

According to NIST’s call for participation, the Cyber AI Profile emphasizes three attributes:

  • Explainability: Security teams need to know why a model flagged an event, what features influenced a decision, and how to reproduce it. This builds analyst trust, accelerates triage, and supports audits.
  • Robustness: Models must withstand adversarial perturbations, evasion techniques, and data poisoning. Expect guidance on adversarial testing and secure training pipelines.
  • Privacy: Training and inference must protect sensitive user and operational data. Techniques like differential privacy, minimization, and secure enclaves will likely feature.

These pillars echo the NIST AI RMF’s focus on trustworthy AI — now grounded in concrete cyber use cases.

Where the Cyber AI Profile Fits in the NIST Landscape

If you’re already using NIST standards, here’s the mapping you’ll care about:

  • To NIST CSF 2.0: The Profile will likely articulate AI-specific objectives under each Function (Identify, Protect, Detect, Respond, Recover) with examples and outcomes aligned to cybersecurity goals.
  • To NIST AI RMF: It translates high-level AI trustworthiness concepts into SOC-ready controls, telemetry, and guardrails.
  • To NIST SP 800-53 and related controls: Expect mappings to control families like Access Control (AC), Audit and Accountability (AU), System and Information Integrity (SI), and Supply Chain Risk Management (SR). See SP 800-53 Rev. 5: https://csrc.nist.gov/publications/detail/sp/800-53/rev-5/final

The payoff: fewer gaps between your AI pilots, your cyber controls, and your auditors.

Real-World Use Cases the Profile Will Likely Cover

Here’s where AI is already transforming cyber — and where a profile will standardize best practices:

  • SIEM/XDR signal quality
  • ML-based anomaly detection to cut noise and spot lateral movement.
  • Semantic correlation across logs to surface multi-stage attacks.
  • Explainable models that show feature importance and causal links.
  • Incident response and SOAR
  • Automated enrichment, case grouping, and guided triage.
  • Playbooks that adapt based on historical outcomes; human-in-the-loop approvals.
  • LLM-based copilot support for drafting tickets, scripts, and communications — with guardrails.
  • Vulnerability and patch management
  • Risk-based prioritization using exploit telemetry, asset criticality, and exposure.
  • Predictive patching recommendations and maintenance window planning.
  • Identity and access analytics
  • UEBA for compromised accounts and privilege misuse.
  • Risk-adaptive access policies informed by behavioral baselines.
  • Email and web security
  • Phishing detection that adapts to novel lures.
  • Domain and sender reputation modeling enriched by external threat intel.
  • Cloud and container security
  • Drift detection in IaC and runtime; anomaly detection in service-to-service behavior.
  • AI-assisted policy generation for Kubernetes and serverless architectures.
  • Data loss prevention and privacy
  • Content classification and sensitive data discovery with configurable explainability.
  • AI-assisted data minimization and tokenization strategies.

Each of these will benefit from consistent controls for data provenance, model validation, adversarial testing, logging, and auditability.

Threats the Cyber AI Profile Will Help You Manage

AI changes the threat model. Expect guidance on mitigating:

  • Data/model poisoning: Attackers seed training or fine-tuning data with malicious patterns. Mitigations: robust data pipelines, signed datasets, outlier detection, canary data, and traceability.
  • Adversarial evasion and perturbations: Small input tweaks cause misclassification. Mitigations: adversarial training, ensemble models, input sanitization, runtime detectors.
  • Model theft and inversion: Exfiltration of model parameters or reconstruction of sensitive training data. Mitigations: access control, rate limiting, watermarking, and privacy-preserving learning.
  • Prompt injection and jailbreaks (for LLM-based tools): Malicious content manipulates the model to reveal secrets or take unsafe actions. Mitigations: content filtering, system prompt hardening, tool-use constraints, and output validation.
  • Hallucinations and automation surprise: Confidently wrong outputs drive poor decisions. Mitigations: retrieval grounding, restricted action spaces, confidence scoring, and human approvals for high-impact steps.
  • Supply chain risks: Third-party models, datasets, and components introduce vulnerabilities. Mitigations: model cards, SBOM/MBOM-like artifacts, signature verification, and vendor risk assessments.
  • Drift and degradation: Changes in environments cause model performance to decline. Mitigations: continuous monitoring, feedback loops, retraining cadences, and rollback plans.

For additional context on adversarial ML, see MITRE ATLAS: https://atlas.mitre.org and the OWASP Top 10 for LLM Applications: https://owasp.org/www-project-top-10-for-large-language-model-applications/

How the Profile May Structure Maturity and Controls

While the final content will emerge from the community process, many teams should expect:

  • CSF-aligned maturity tiers
  • Entry: Pilots with documented risks and manual oversight.
  • Risk-informed: Basic controls for data governance, model evaluation, and change management.
  • Repeatable: Standardized pipelines, adversarial testing, and continuous monitoring with KPIs.
  • Adaptive: Threat-informed models, automated mitigations, strong provenance, and robust auditability.
  • Control domains likely to appear
  • Governance: Roles, RACI for AI in cyber, policy baselines, and exception handling.
  • Data lifecycle: Collection, labeling, lineage, quality gates, and retention/minimization.
  • Model lifecycle (MLOps): Versioning, reproducible training, peer review, and approval workflows.
  • Robustness and testing: Red-teaming, adversarial evaluations, fuzzing, and safe failure modes.
  • Privacy and security: Access control, encryption, privacy-preserving computation, and incident response.
  • Human factors: Explainability standards, analyst education, escalation pathways, and cognitive load management.
  • Monitoring and logging: Telemetry coverage, drift detection, bias/performance metrics, and traceable decisions.
  • Supply chain: Third-party vetting, SBOM/MBOM, licensing, and model provenance.
  • Interoperability: Open standards for telemetry, alerting, and playbooks.

Practical Steps You Can Start Today

You don’t need to wait for the final profile to make progress. Here’s a readiness checklist:

  • Establish governance
  • Define an AI-in-cyber steering group led by the CISO and head of data/ML.
  • Create a policy addendum: acceptable AI use, data handling, human-in-the-loop thresholds, and audit requirements.
  • Inventory and assess current AI/ML use
  • Catalog models in SIEM/XDR, EDR, SOAR, IAM, and DLP. Capture purpose, inputs/outputs, training data, owners.
  • Score risk by impact, autonomy, data sensitivity, and external dependencies.
  • Tighten data pipelines
  • Implement lineage tracking, schema validation, and dataset signing.
  • Add canary datasets and golden signals for regression detection.
  • Build evaluation harnesses
  • For detection: measure precision/recall, false positives, mean-time-to-detect, and coverage against ATT&CK techniques.
  • For response: measure time-to-contain, automation accuracy, and analyst deflection rates.
  • For LLM assistants: rate answer quality, hallucination rate, grounding rate, and unsafe output frequency.
  • Introduce adversarial testing
  • Test with red-team data, adversarial perturbations, and simulation labs.
  • Incorporate MITRE ATT&CK and ATLAS scenarios into validation cycles.
  • Set human-in-the-loop guardrails
  • Require human approvals for high-impact actions (e.g., account disablement, network isolation).
  • Use confidence thresholds and multiple-model consensus for automated steps.
  • Improve logging and traceability
  • Log prompts, inputs, outputs, model versions, and feature importances alongside security events.
  • Ensure logs are immutable and correlated back to case management systems.
  • Plan for drift
  • Monitor data distributions and model performance; define rollback criteria and retraining triggers.
  • Train your SOC
  • Teach analysts how AI makes decisions, when to trust it, and how to escalate.
  • Create playbooks for AI failure modes and safe overrides.

How This Can Upgrade SIEM/XDR in the Next 12 Months

If you’re targeting measurable improvements:

  • Reduce alert fatigue
  • Implement ranking models with explainability; hide “why” in every alert for analyst trust.
  • Improve lateral movement detection
  • Use graph-based analytics with behavior baselines across identity, endpoint, and network telemetry.
  • Speed triage
  • Automate enrichment with LLM-guided summaries that cite sources (retrieval augmented generation) to minimize hallucinations and boost reliability.
  • Close vulnerability exposure faster
  • Use exploit likelihood models plus asset criticality to prioritize; integrate results into change windows.
  • Prove it works
  • Publish dashboards with precision/recall, mean time to detect/respond, and analyst deflection — tied to business risk.

Standards that can help you integrate: – STIX/TAXII for intel sharing: https://oasis-open.github.io/cti-documentation/ – CACAO for playbook interoperability: https://www.oasis-open.org/committees/cacao/ – MITRE ATT&CK: https://attack.mitre.org

Interoperability and Reference Architectures: Why Participants Will Benefit

NIST’s NCCoE projects typically produce reference designs with commercial technologies working together. Participating organizations often gain:

  • Early access to architectures that show “how to wire this together” across SIEM/XDR, SOAR, IAM, and data platforms.
  • Shared evaluation datasets, test harnesses, and procedures that make vendor comparisons fairer and more transparent.
  • Blueprints for logs, prompts, and telemetry that enable cross-tool correlation — key for explainability and audits.

Learn about joining NCCoE projects here: https://www.nccoe.nist.gov/partners/how-participate

Who Should Consider Participating?

  • Security vendors and MSSPs building AI-driven detection, response, and exposure management.
  • Cloud providers and data platforms that underpin security analytics and AI pipelines.
  • SOC leaders and CISOs from regulated industries who need audit-ready AI.
  • Academia and research labs working on adversarial ML, explainability, and privacy tech.
  • Standards bodies and open-source communities advancing telemetry, playbooks, and model documentation.

Bring your use cases, constraints, and field data. This works best when practitioners shape what “good” looks like.

Governance and Compliance: Getting Ahead of Emerging Requirements

A Cyber AI Profile can help you prepare for:

  • Audit-ready AI operations: Documented controls, metrics, and evidence for model lifecycle, data handling, and decision traceability.
  • Secure-by-design expectations: Strong provenance, least-privilege access to models, and robust failure handling.
  • Crosswalks to existing frameworks: Easier mapping to CSF, SP 800-53, and AI RMF reduces compliance burden without stifling innovation.

Context that may be helpful: – U.S. Executive Order on Safe, Secure, and Trustworthy AI (2023): https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/ – CISA’s Secure by Design: https://www.cisa.gov/securebydesign

What “Trustworthy AI in the SOC” Looks Like

Here’s a practical target state the Profile may help define:

  • Every model has an owner, purpose statement, training data lineage, and version history.
  • Decisions are explainable: alerts include feature importances or summaries with citations.
  • Critical actions require human approval or pass confidence and consensus checks.
  • Adversarial test suites run pre-deployment and continuously in production shadows.
  • Telemetry is complete: inputs, outputs, prompts, and model versions are logged and reviewable.
  • Privacy is preserved: sensitive fields are minimized, masked, or processed in secure enclaves; synthetic data is used safely where appropriate.
  • Drift is managed: performance SLAs, retraining cadences, rollback plans, and canary deployments are standard practice.
  • Third-party risk is visible: model cards, SBOM/MBOM artifacts, and signed components are table stakes.

Anticipated Deliverables From the Cyber AI Profile

While exact outputs will come from the working group, typical NCCoE and NIST deliverables include:

  • Use-case playbooks with reference integrations.
  • Control mappings to CSF and, where applicable, SP 800-53.
  • Test plans, datasets, and evaluation metrics.
  • Implementation guidance and example configurations.
  • Interoperability patterns and data schemas.
  • Case studies and measurement results.

These are the kinds of assets security teams can adopt with minimal customization — a big accelerant for programs stuck in pilot purgatory.

How to Engage With the Project

  • Read the announcement and participation details: NIST: Participate in the development of a new Cyber AI Profile
  • Nominate your organization
  • Identify domain experts (SOC engineering, data/ML, governance/risk).
  • Prepare summaries of your AI use cases, datasets, risks, and lessons learned.
  • Offer reference implementations
  • If you’re a vendor or open-source maintainer, propose integrations and test harnesses.
  • If you’re an end user, bring real-world telemetry (appropriately sanitized) and evaluation scripts.
  • Commit to openness
  • NCCoE work often results in publicly available guidance. Align your internal processes so contributions can be shared.

A Sample Roadmap for Your Organization (Next 180 Days)

  • Days 1–30: Inventory, governance, and risk triage
  • Catalog AI features, assign owners, and implement minimal logging standards.
  • Days 31–90: Controls and evaluation
  • Stand up an evaluation harness; introduce adversarial tests; define human-in-the-loop thresholds.
  • Days 91–120: Interoperability and documentation
  • Normalize telemetry; create model cards; align to CSF objectives; map to SP 800-53 where applicable.
  • Days 121–180: Scale and measure
  • Pilot in one SOC workflow (e.g., phishing triage); publish KPIs; plan retraining and rollback processes.
  • Ongoing: Participate in NIST’s working sessions to align your roadmap with emerging guidance.

Common Pitfalls to Avoid

  • Treating AI as a black box: Without explainability and logs, you can’t trust, tune, or audit.
  • Skipping data governance: Bad or toxic data leads to brittle models and hidden bias.
  • Over-automation: Keep humans in critical loops until models are proven under adversarial conditions.
  • Underestimating supply chain risk: Vet third-party models and datasets like you would any software component.
  • Ignoring drift: What works today may degrade quietly; set alerts and retraining SLAs.

FAQs

  • What exactly is a “Cyber AI Profile”?
    It’s a CSF-aligned, community-built set of controls, practices, and reference designs for safely and effectively using AI in cybersecurity operations.
  • Who can participate?
    NIST encourages participation from industry vendors, service providers, end-user organizations, academia, and government. See NCCoE participation guidance: https://www.nccoe.nist.gov/partners/how-participate
  • Will this replace the NIST AI RMF?
    No. The AI RMF establishes broad principles for trustworthy AI. The Cyber AI Profile applies those principles to cybersecurity use cases with concrete controls and examples.
  • How does this relate to NIST CSF 2.0?
    The Profile will map AI-enabled capabilities and safeguards to CSF Functions and categories, helping organizations implement AI in ways that align with established cyber outcomes.
  • Is there a cost to join?
    NIST projects typically do not charge participation fees, but participants contribute time, expertise, and, in some cases, technology or data. Refer to the announcement for specifics.
  • What about confidential data?
    Participants generally share sanitized or synthetic data and follow NCCoE policies. Always consult your legal and compliance teams before sharing.
  • Will the Profile recommend specific vendors or products?
    NIST focuses on capabilities and interoperability, not endorsing particular vendors. Reference architectures often demonstrate multiple interoperable solutions.
  • When will deliverables be available?
    Timelines are determined during the project. Early drafts and public comment periods are common in NIST processes.
  • How can this help with compliance?
    Expect clearer evidence requirements (logs, model cards, evaluation results) and control mappings that make audits more straightforward.
  • We’re early in our AI journey. Is this still relevant?
    Absolutely. The Profile can help you start with good governance, avoid common missteps, and invest in architectures that scale responsibly.

The Bottom Line

AI is now integral to cyber defense — but only if it’s trustworthy, explainable, and resilient under attack. NIST’s Cyber AI Profile aims to turn scattered best practices into a shared, CSF-aligned playbook for the SOC, complete with controls, maturity guidance, and reference architectures.

If you want your SIEM/XDR stack to be smarter without becoming a black box, if you need audit-ready AI without slowing innovation, and if you believe interoperability beats lock-in, this is your opportunity to help set the standard.

Take the next step: – Read the announcement: NIST: Participate in the development of a new Cyber AI Profile – Align your internal roadmap to CSF 2.0: https://www.nist.gov/cyberframework – Ground your practices in the AI RMF: https://www.nist.gov/itl/ai-risk-management-framework – Engage with NCCoE to contribute and learn: https://www.nccoe.nist.gov/partners/how-participate

Clear takeaway: The Cyber AI Profile will help defenders move faster and safer with AI — balancing innovation and resilience. Get involved early to shape practical guidance that your team can use, your auditors can trust, and your adversaries will hate.

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