Prompt Injection & Jailbreaks
Direct and indirect prompt injection, jailbreak chains and instruction override against LLM applications and agents. OWASP LLM01 · MITRE ATLAS.
Independent assurance across two layers — AI governance, anchored on ISO/IEC 42001 and NIST AI RMF, with EU AI Act readiness and the equivalent regimes across the EU, Brazil, US, UK and Singapore; and AI threat intelligence, including MITRE ATLAS, OWASP LLM Top 10, adversarial red teaming and AI-specific threat modeling — for regulated financial institutions deploying or procuring AI systems. The output is a structured, evidenced assurance report formatted for supervisory review, board reporting and legal defensibility.
The challenge
AI regulation is arriving across every major market — binding in the EU, advancing in Brazil, the US, the UK and Singapore — and converging on a common expectation: documented, independent evidence that your AI is governed and defensible. If you are deploying or procuring AI systems in a regulated context and cannot evidence a structured risk assessment against recognised standards, you are already behind the curve — wherever you operate.
AI regulation now imposes obligations on high-risk AI systems across major jurisdictions, with international standards (ISO/IEC 42001, NIST AI RMF) setting the assurance bar. Financial institutions running credit scoring, fraud detection or customer-facing AI face requirements most have not yet documented to the required evidentiary standard.
AI governance assessments conducted by the same teams that built or deployed systems carry inherent conflicts that regulators identify immediately. Independent validation with traceable methodology and documented evidence is now the standard regulators and audit committees require.
The two layers
AI governance and AI threat intelligence answer different questions. Governance asks whether AI is used in a controlled, accountable and auditable way. Threat intelligence asks which real and emerging attacks could compromise your models, agents, data and automated decisions. A defensible AI programme needs both — one defines the control system, the other keeps it informed by live adversarial activity.
The governance, risk and accountability layer over how AI is used, developed or procured — policies, system inventory, risk classification, controls and documented evidence of accountability.
Frameworks: ISO/IEC 42001 · NIST AI RMF · EU AI Act
Audience: board, risk, compliance, legal, audit, DPO
The technical-operational layer — identifying, testing and anticipating adversarial and technical threats against AI systems, and threats enabled by AI, from prompt injection to model abuse.
Frameworks: MITRE ATLAS · OWASP LLM Top 10 · red teaming
Audience: CISO, SOC, AppSec, architecture, engineering
Where the two meet is AI security governance: governance defines the control system; threat intelligence supplies the real and emerging threats that system must address. Without threat intelligence, AI governance becomes documentation. Without governance, AI threat intelligence stays technical and carries no institutional accountability.
Engagement models
EONTA delivers AI governance assurance across two structured tracks — risk classification and management-system assurance — anchored on ISO/IEC 42001 and NIST AI RMF and mapped to the regimes that apply to you.
Independent classification of your AI systems using the NIST AI RMF risk model and the EU AI Act taxonomy — prohibited, high-risk, limited-risk and minimal-risk — mapped to equivalent tiers in Brazil, the US, UK and Singapore, with documented evidence for each determination.
Classification documented and traceable
Aligned to ISO/IEC 42001, NIST AI RMF and current implementing acts
Designed for regulatory review and submission
Formal assurance over your AI Management System — design effectiveness, control implementation, and audit-ready documentation aligned to ISO/IEC 42001, with the NIST AI RMF functions (Govern, Map, Measure, Manage) as the operating model.
Independent — no implementation conflict
Evidence-based — not advisory opinion
Structured for regulatory submission
AI threat intelligence
Identify, monitor and assess adversarial threats targeting AI models, LLM applications, autonomous agents, data pipelines and AI-enabled decision systems. We map real-world AI attack techniques to MITRE ATLAS and the OWASP LLM Top 10, validated through adversarial red teaming and AI-specific threat modeling.
Direct and indirect prompt injection, jailbreak chains and instruction override against LLM applications and agents. OWASP LLM01 · MITRE ATLAS.
Training-data and context leakage, system-prompt exposure and unintended disclosure through model outputs. OWASP LLM02 / LLM06.
Theft of model behaviour, membership-inference and inversion attacks that reconstruct sensitive training data. MITRE ATLAS exfiltration.
Training-time and fine-tuning integrity attacks, backdoors and supply-chain poisoning of models, datasets and embeddings. MITRE ATLAS.
Over-privileged autonomous agents, unsafe tool and function calling, and insecure plugin / API integrations. OWASP LLM06 / LLM08.
Deepfakes, synthetic identity, automated social engineering and large-scale abuse of AI systems for financial crime.
Scenario-based adversarial testing of models, LLM applications and autonomous agents, plus AI-specific threat modeling across the model, prompt, data and tooling layers.
Board-ready intelligence that translates technical findings into prioritised, defensible risk decisions — not a tool dump.
Core capabilities
Each capability delivers structured, evidence-based evaluation against current regulatory requirements and ISO standard obligations.
Scope mapping of all AI systems, use-cases, and data flows — establishing the foundation for accurate risk classification.
Formal classification under the EU AI Act Annex III taxonomy, the NIST AI RMF risk model and equivalent regional tiers, with rationale defensible to the AI Office and national regulators.
Assessment of your AI Management System design and operating effectiveness against the full ISO 42001 control set.
Validation of human oversight, transparency and monitoring controls mapped to the NIST AI RMF functions and trustworthy-AI characteristics.
Structured evidence packages — traceability matrices, control assessments, and risk registers — ready for regulatory review.
Automated decision-making and data-protection obligations across the regimes that apply to you, in the context of high-risk AI deployment in your jurisdictions.
Service framework
The information procurement and risk management teams need before approving an external assurance engagement.
How it works
A structured four-phase engagement calibrated to ISO/IEC 42001, NIST AI RMF and the regulatory timelines that apply across your jurisdictions.
Map all AI systems, use-cases, data inputs, and decision outputs across your organisation.
Apply the EU AI Act, NIST AI RMF and equivalent regional risk taxonomies to each system with documented, auditable rationale.
Evaluate governance controls, human oversight, and ISO 42001 / NIST AI RMF alignment against current implementation.
Produce audit-ready evidence packages — traceability matrices, findings reports, and regulatory-ready summaries.
Why EONTA
EONTA's AI assurance methodology is built around the specific regulatory environment financial institutions operate in — not generic compliance checklists. EU AI Act classification in financial services carries sector-specific nuance that generic frameworks miss.
We produce documented, traceable evidence packages — not advisory opinions. The difference matters when regulators ask to see your compliance rationale, not just your policy statement.
EONTA does not build AI systems, train models, or provide AI consulting. Our only interest is the quality of your governance assurance — which is precisely why our conclusions can be trusted.
Who this is for
EONTA's AI assurance services are designed for the governance functions and executive roles directly accountable for AI compliance, risk oversight, and regulatory standing.
Primary stakeholders
Common engagement triggers
AI Act enforcement timeline approaching
Organisations seeking to classify high-risk AI systems and establish governance documentation before regulatory deadlines.
Regulator or audit committee challenge
Governance functions requiring independent validation of AI governance quality following internal or external scrutiny of AI systems.
New AI system deployment
Institutions deploying new AI in credit, insurance, or customer-facing roles requiring classification and governance assurance before go-live.
Frequently asked
Take the next step
Most organisations operating AI in financial services don't have a documented answer. A scoping call with EONTA takes 45 minutes and changes that.
All scoping conversations are confidential. EONTA does not share engagement details with third parties.