Secure the AI making decisions inside your business
Credit, fraud, AML, onboarding, customer support, and risk workflows are increasingly powered by AI models and agentic systems. Their training data, feature inputs, prompts, model artefacts, inference environments, and agent actions are now attack surfaces. SISA helps financial institutions test AI against adversarial manipulation, secure AI systems at runtime, govern agentic access, and prove that AI controls hold beyond initial validation.
Inside an AI attack chain: how model, prompt, supply-chain, and agentic risks converge in BFSI
THE STAKES
When AI makes consequential financial decisions, the attack surface shifts from the transaction to the inputs that shape the model's behaviour and the agents that act on its outputs. Validating a model's accuracy or fairness, proves it behaves under expected conditions; it says nothing about whether it holds when training data, feature inputs, and inference context are actively manipulated.
Attackers probe decision boundaries with iterative submissions to reverse-engineer fraud and credit logic, then craft inputs that pass at minimum cost without tripping a single conventional alert.
Malicious instructions embedded in documents, emails, and web content can drive enterprise AI agents to act outside their intended boundaries the document is the carrier, the agent is the executor.
AI agents increasingly operate with service-account-grade rights yet sit outside the governance applied to privileged users. A manipulated agent is a privileged insider.
Adversarial LLMs generate polymorphic malware and fraudulent documents that are structurally unique each iteration, defeating signature detection — while AI-scale social engineering, deepfakes, and synthetic identities target KYC and onboarding directly.
An AI breach is rarely one bad input. It's gaps aligning across the model's build and runtime what SISA calls an attack chain. The question isn't whether your model passed its accuracy and bias tests; it's whether it holds when an adversary actively manipulates its inputs. Securing AI means continuously verifying integrity and behavior, not validating a model once before deployment.
AI BREACHES ARE ATTACK CHAINS, NOT SINGLE BAD INPUTS
An AI incident rarely begins and ends with one manipulated prompt or one poisoned input.
It develops as weaknesses align across data, model, prompt, artefact, inference, agent, governance, and monitoring layers. A model may be accurate in testing while its feature inputs can be manipulated. An AI assistant may perform correctly under normal prompts while failing under prompt injection. A model artefact may be deployed without integrity validation. An AI agent may inherit privileges no access-review process governs.
Built on insights from real security investigations and payments insights, SISA helps institutions identify and reduce AI attack chains across the full lifecycle from model build and deployment to runtime behaviour, agentic access, and governance evidence
How SISA secures AI
SISA addresses each layer where AI turns from asset into attack surface across both how models are built and how they behave at runtime.
Test models the way an adversary attacks them
Probes models the way attackers do decision-boundary probing, feature perturbation, evasion construction so you find the inputs that defeat your fraud and credit logic before someone uses them against you.
Harden the model against prompt injection and abuse
Targets the inference-time vector directly prompt injection, instruction manipulation, and outputs that drift beyond intended boundaries closing the gap where agents act on adversarial content.
Verify model and supply-chain integrity
Verifies training-data provenance, model weights, and ML dependencies treating the AI supply chain as the regulated software supply chain it has become, and catching poisoning and tampering before deployment.
Watch AI in production
Surfaces adversarial probing, anomalous inference, and live attack-surface changes in real time so a model under attack shows up as a signal, not a downstream consequence.
Bring AI agents under privileged-identity monitoring
Treats agentic AI as the privileged non-human identity it is, applying the same continuous behavioral monitoring to an agent's actions as to any high-privilege account.
Govern AI as a controlled, evidenced capability
Turns adversarial-robustness testing, reassessment cadence, and human-in-the-loop thresholds into a governed, board-ready posture aligned with where supervisory expectations are heading.
Build the team that secures it
SISA's compliance and risk advisory wraps the technical controls in governance mapping AI assurance to the frameworks and supervisory expectations your institution answers to.
One view of AI security and governance
AI risk develops in the gaps between model behaviour, training data, prompts, runtime activity, supply-chain integrity, agentic access, and governance evidence.
Through OneLens™, SISA One unifies these capabilities into a single, board-ready picture of AI exposure surfacing adversarial weakness, prompt-injection risk, model and artefact integrity gaps, runtime anomalies, AI attack-surface changes, agentic-identity risk, and governance evidence.
Start with the AI system or risk area that matters most today. Expand as your AI estate grows.
The AI Attack-Path Review
A focused engagement with SISA’s AI security, governance, and forensic experts. You get:
A mapped view of the models, agents, prompts, data flows, artefacts, and integrations that create AI exposure in your environment
An adversarial testing report showing how AI systems behave under manipulation, evasion, prompt injection, or abuse
Recommendations aligned to BFSI security, privacy, compliance, and supervisory expectations
A 1:1 debrief session with a SISA AI security expert
Related Use Cases
Supply Chain & Third-Party Risk
Ransomware Prevention
Insider Threat & Privileged Access
Central Bank Compliance
Find out whether your models hold under adversarial pressure before they make the wrong decision.
FAQs
AI security is the protection of AI systems, models, prompts, data, artefacts, APIs, agents, and runtime environments from manipulation, abuse, evasion, leakage, poisoning, and compromise.
Financial institutions use AI in areas such as fraud detection, credit decisioning, AML, onboarding, customer support, risk scoring, and operations. If these systems are manipulated or poorly governed, the impact can affect customers, compliance, fraud losses, and institutional trust.
AI security focuses on preventing, detecting, and responding to attacks against AI systems. AI governance focuses on policies, controls, accountability, risk classification, monitoring, evidence, and oversight. BFSI institutions need both.
Adversarial AI testing evaluates whether an AI model can be manipulated through crafted inputs, feature changes, evasion attempts, decision-boundary probing, prompt injection, or other attack techniques. It shows how the model behaves under hostile conditions, not only under expected usage.
Prompt injection is an attack where malicious instructions are inserted into prompts, documents, emails, web pages, or other content processed by an AI system. The goal is to make the model or agent ignore its intended instructions, reveal information, or perform unauthorized actions.
AI supply-chain risk arises from the training data, model artefacts, weights, libraries, dependencies, plugins, APIs, and tools used to build and run AI systems. If any part of this chain is poisoned, tampered with, or unverified, the AI system may become unsafe or unreliable.
AI agents can interact with applications, data, workflows, and tools. If they have broad permissions, they can act like non-human privileged identities. If manipulated or compromised, they can perform actions that create security, privacy, or operational risk.
SISA helps test models against adversarial manipulation, harden LLMs against prompt injection and abuse, verify model and supply-chain integrity, monitor AI behaviour at runtime, and govern AI systems through structured evidence and control workflows.
SISA helps institutions inventory AI systems, classify risk, map controls to applicable frameworks, define human oversight, manage reassessment cadence, track remediation, and produce governance evidence for leadership, auditors, and regulators.
Yes. SISA’s AI security and governance capabilities are designed to complement existing AI development, governance, identity, security monitoring, compliance, and risk-management investments rather than replacing them.