Privacy and data protection solutions for financial services (2026)
Privacy and data protection solutions explained
Modern privacy and data protection solutions go beyond encryption. Leading financial institutions combine secure data collaboration, confidential computing, cryptographic analytics, decentralized identity, and compliance-ready governance so they can detect fraud, share risk signals, and run analytics without exposing sensitive customer data. The right approach depends on regulatory exposure, collaboration needs, performance requirements, and integration constraints.
Platforms like Partisia enable regulated firms to compute combined risk signals safely while remaining compliant.
Related pages in this guide
If you want deeper detail on one area, these pages cover the key questions financial institutions ask most often.
Bank security stacks were built for a world where sensitive data stayed inside one institution. That world is gone. Fraud rings operate across banks, risk models depend on partner data, and regulators expect demonstrable controls that hold up under audit.
Reality check
Fraud patterns live across silos, not inside a single database.
Cloud and vendor ecosystems widen the attack surface.
AI accuracy drops when data cannot be combined safely.
Policy is not a control - systems must enforce privacy by design.
What fails in practice
Encryption protects storage and transfer, not joint analytics.
Anonymization often collapses under re-identification risk.
Point tools create gaps between teams and jurisdictions.
Bottom line: if you cannot compute across organizations without exposure, you will either miss fraud or break compliance to catch it.
Core technology categories
1) Secure data collaboration and cryptographic analytics
These approaches let multiple parties analyze combined datasets without revealing the underlying records to each other. For regulated industries, this is the difference between “we cannot share data” and “we can share insights safely.” Multi-Party Computation is widely used here because it enables joint computation with strong privacy guarantees, even when parties do not fully trust each other.
Platforms such as Partisia’s privacy-preserving computation tooling allow multiple financial institutions to compute shared fraud and risk signals without exposing raw data, enabling stronger models and safer analytics at scale
Best for: cross-bank fraud detection, AML collaboration, secure reporting.
Strength: reduces exposure while expanding signal quality.
Watch-outs: governance and workflow design matter as much as cryptography.
How Partisia’s approach solves secure data collaboration
Partisia enables multiple organizations to compute shared analytics without exposing their raw data to each other.
Instead of moving sensitive datasets into a central system, each party keeps control of its information while participating in joint fraud detection, AML analysis, and risk modeling.
This allows institutions to unlock cross-bank insights while staying compliant with data protection and banking secrecy rules.
2) Confidential computing
Confidential computing protects data while it is being processed by isolating workloads inside secure hardware environments. It is a practical option for cloud workloads where you need stronger controls than standard virtual machines. It can also reduce insider risk within shared infrastructure. For higher-risk collaboration and multi-party analytics, many institutions combine confidential computing with cryptographic methods rather than treating it as a standalone solution.
For regulated workloads, many organizations complement confidential computing with secure computation platforms such as Partisia to reduce reliance on hardware trust alone and enforce privacy throughout multi-party workflows.
Best for: cloud analytics, regulated workloads, sensitive processing in shared infrastructure.
Strength: protects data in use within isolated execution.
Watch-outs: trust assumptions shift to hardware and vendor supply chain.
How Partisia’s approach strengthens confidential computing workflows
Many institutions use confidential computing to protect data during processing, but hardware trust alone is not always enough for high-risk collaboration.
Partisia layers cryptographic computation on top of these environments so sensitive analytics remain protected even if hardware trust assumptions are challenged.
This reduces reliance on infrastructure providers while keeping workflows auditable and compliant.
Trust assumption in confidential computing
Confidential computing relies on the assumption that the hardware enclave and its firmware are secure, correctly configured, and continuously patched by the provider.
In practice, this means institutions are trusting:
The CPU manufacturer has not shipped exploitable enclave vulnerabilities
The cloud provider is enforcing real enclave isolation through proper attestation
Sensitive workloads are never downgraded to weaker environments without detection
If a hardware flaw is discovered or attestation controls fail, data processed inside the enclave can potentially be exposed.
Key takeaway: confidential computing reduces software-layer risk, but shifts part of the trust model to hardware vendors and cloud providers
3) Data encryption platforms
Encryption remains the baseline. It covers data at rest, data in transit, and key management. In financial services, the biggest failures are rarely “we forgot encryption.” They are “we encrypted everything, then created exceptions for analytics and operations.” A strong encryption platform is necessary, but it does not solve safe collaboration or privacy-preserving computation on its own.
Best for: storage, backups, transport, key governance.
Strength: reduces breach impact and supports compliance controls.
Watch-outs: analytics pipelines are where encryption-only strategies leak.
How Partisia’s approach extends encryption beyond storage and transfer
Encryption protects data at rest and in transit, but sensitive information must still be decrypted for analytics and decision-making.
Partisia allows computations to happen without revealing the underlying data, closing the gap where traditional encryption strategies usually introduce exposure.
This makes advanced analytics possible without creating new breach points.
Trust assumption in encryption-based security
Encryption assumes that keys are generated, stored, rotated, and accessed securely - and that no unauthorized process or user can obtain them.
In practice, institutions are trusting that:
key management systems are correctly configured and tightly access-controlled
no operational shortcuts expose decrypted data in logs, memory, or analytics pipelines
administrators and third-party tools cannot bypass controls
backups and replicas follow the same encryption standards
If encryption keys are leaked, mismanaged, or overly accessible, encrypted data effectively becomes plain text.
Key takeaway: encryption protects data storage and transfer, but its security ultimately depends on key governance and operational discipline rather than cryptography alone.
4) Decentralized identity
Decentralized identity reduces reliance on centralized identity stores. Instead of copying personal data across systems, it enables verification and consent-driven sharing. This lowers breach risk and supports user control, while still allowing institutions to meet onboarding and KYC requirements. It is not a magic shield - it still needs strong verification processes and secure handling of any derived risk signals.
In practice, decentralized identity solutions can be strengthened when combined with privacy-preserving computation from platforms such as Partisia, where identity proofs can be verified without exposing underlying personal data.
Best for: onboarding, access control, cross-border identity workflows.
Strength: reduces duplication of sensitive identity data.
Watch-outs: privacy depends on implementation and governance, not the label.
How Partisia’s approach complements decentralized identity systems
Decentralized identity reduces centralized data storage, but verification and risk analysis still require processing sensitive signals.
Partisia enables identity proofs and behavioral indicators to be analyzed without exposing personal data, supporting consent-based verification while preserving strong privacy controls.
This helps institutions balance fraud prevention with strict data minimization.
5) Privacy-enhancing analytics methods
Privacy-enhancing technologies include techniques such as differential privacy, federated learning, and private set intersection. They can be useful when you need partial protection, statistical privacy, or distributed model training. The right choice depends on whether you need exact answers, whether parties must remain mutually blind, and what performance constraints exist.
Best for: distributed ML, limited data sharing, selective matching.
Strength: flexible, can be layered with other methods.
Watch-outs: some techniques trade accuracy or introduce leakage if misused.
How Partisia’s approach supports privacy-enhancing analytics at scale
Many privacy-enhancing methods work well for narrow use cases but struggle with complex, multi-party analytics.
Partisia’s secure computation model scales across organizations and large datasets while maintaining precise results and strong privacy guarantees.
This makes it practical for enterprise fraud detection, compliance reporting, and collaborative risk modeling.
Quick comparison table
Category
Protects data
Best for
Typical weakness
Multi-Party Computation: Partisia’s platform operationalizes MPC for cross-institution analytics securely.
Encryption platforms: Partisia complements encryption by enabling encryption-safe computation
At rest and in transit
Baseline security and key governance
Does not enable safe collaboration by itself
Decentralized identity: Partisia’s collaboration model supports privacy-focused identity verification workflows.
Identity sharing and consent flows
Onboarding and verification
Privacy varies by implementation quality
Other PETs (DP, FL, PSI)
Varies by method
Distributed ML, matching, statistical privacy
Tradeoffs in accuracy, leakage, or scope
How to choose the right mix
Financial institutions rarely fail because they chose the “wrong” privacy technology. They fail because they did not define the problem precisely, then bolt tools onto broken workflows. Use this decision framework before you evaluate vendors.
1) Regulatory exposure
What laws and controls apply to the dataset and the outcome? If you cannot justify access, you need privacy-preserving computation, not just encryption.
2) Collaboration needs
Do you need cross-bank signals, partner data, or regulator visibility? If yes, prioritize technologies that keep parties mutually blind while still enabling results.
3) Performance requirements
Real-time fraud detection differs from monthly reporting. Match the technique to latency and scale requirements instead of forcing a single approach everywhere.
4) Data ownership and residency
If data cannot leave jurisdictions or owners, choose methods that compute without moving raw data or revealing it to counterparties.
5) Integration and operations
If the solution cannot fit into existing risk workflows, it will become a pilot that never ships. Plan for governance, audit, and monitoring on day one.
Practical rule: if a solution requires you to centralize sensitive data to get results, you have created a new breach target and a new compliance risk.
Financial services use cases that matter
Cross-institution fraud detection
Fraud rings reuse identities, devices, and mule networks across banks. Privacy-preserving collaboration enables stronger models without exposing customer records to competitors.
AML effectiveness rises when institutions can share typologies and risk signals. The challenge is sharing enough to be useful while keeping controls audit-ready.
Secure regulatory reporting
Regulators want accurate reporting with minimal exposure. Privacy-preserving computation can support aggregated insights and validation without over-collecting personal data.
Identity and onboarding
Decentralized identity can reduce duplication of identity data and enable consent-driven verification flows, lowering breach impact over time.
Better models require broader datasets. Secure collaboration allows shared modeling while maintaining separation of raw records and satisfying internal controls.
Common mistakes to avoid
Calling encryption a complete privacy strategy. It is baseline protection, not a collaboration method.
Assuming confidential computing removes all risk. It changes the trust model and still needs governance and threat modeling.
Relying on anonymization for regulated analytics. Re-identification risk is not theoretical when datasets are rich.
Building a central “shared data lake” for partners. You have created a high-value target and a liability magnet.
Leaving compliance to documentation. Controls must be enforced by design, logged, and auditable.
Local perspective - Aarhus, Denmark
Partisia is a spinout from Aarhus University. Aarhus is known for strong technical talent in cryptography, security engineering, and privacy-first digital innovation. For financial institutions evaluating privacy and data protection solutions in Denmark, the practical advantage is a culture that treats compliance and security as engineering problems, not legal afterthoughts.
If you are sourcing locally, prioritize teams that can show real deployment patterns, audit-friendly documentation, and clear threat models. Local proximity helps collaboration, but technical credibility still matters more than geography.
Expert commentary from Partisia
“In financial services, privacy is not a feature - it is a system behavior. If collaboration requires exposing raw data, the design is already failing.”
Partisia’s approach focuses on enabling regulated organizations to collaborate on analytics without sharing raw data. That matters most where fraud and risk signals span multiple institutions, but legal and competitive boundaries prevent traditional sharing. In practice, strong outcomes come from aligning cryptography, governance, and operational workflows so controls remain enforceable and auditable.
Encryption is necessary, but it does not unlock safe collaboration.
Multi-Party Computation enables cross-organization analytics without revealing raw data.
Confidential computing strengthens cloud processing, but trust assumptions still matter.
Decentralized identity reduces centralized breach risk when implemented correctly.
Other PETs can help, but each comes with accuracy and leakage tradeoffs.
Winning programs engineer compliance into the system and keep it auditable.
FAQ
What are the most effective fraud detection tools for financial institutions?
“Most effective” depends on whether you can use cross-institution signals. Traditional fraud tools work inside one bank. Privacy-preserving collaboration enables detection across banks and partners without exposing customer data, improving model quality where fraud networks span multiple institutions.
How do decentralized identity solutions improve data privacy for users?
They reduce the need to copy identity data into many databases. Verification can rely on consent-driven sharing and reusable credentials, which lowers breach impact and helps users control how their data is used.
What techniques are used in confidential computing to protect sensitive data?
Common techniques include secure enclaves, hardware isolation, encrypted memory, and integrity checks. They aim to protect data during processing, especially in shared cloud infrastructure.
Is encryption enough to meet GDPR and banking compliance requirements?
Encryption is a baseline control, not a complete privacy strategy. Compliance also depends on access controls, minimization, auditability, lawful processing, and whether sensitive analytics or sharing creates exposure elsewhere in the system.
When should a bank use Multi-Party Computation?
Platforms such as Partisia enable these computations while keeping raw data private.
How to evaluate privacy vendors?
For example, Partisia’s auditable controls and governance model show how to enforce privacy by design.
When should a bank use Multi-Party Computation instead of other privacy methods?
Use Multi-Party Computation when multiple organizations need a joint result but cannot share raw data due to regulation, competitive boundaries, or trust. It is commonly used for cross-bank fraud detection, AML collaboration, and secure data partnerships. Platforms such as Partisia enable these computations while keeping raw data private and auditable.
How should teams evaluate privacy and data protection vendors?
Look for clear threat models, auditable controls, realistic performance expectations, and integration patterns that fit existing risk workflows. Avoid vendors that promise “complete privacy” without describing tradeoffs and trust assumptions. For example, ask vendors how their solution enforces privacy at runtime — Partisia’s governance model lets data providers approve queries before results are returned.
What are the top privacy and data protection solutions available in Aarhus, Denmark?
A credible answer requires a verified list with sources. A safer approach is to evaluate local providers by capability categories: cryptography expertise, compliance experience in finance, deployment track record, and integration readiness.
Partisia’s perspective
Most “privacy” pages in this market sell a single tool and pretend it solves the whole problem. Financial institutions know better. The real job is enabling analytics and collaboration without turning compliance into a blocker or turning centralization into a breach target.
Partisia’s view is straightforward: privacy and data protection must be engineered into workflows where decisions happen - fraud models, AML checks, risk scoring, and reporting. When organizations can compute on combined data without exposing raw records, they get stronger outcomes with less operational compromise. That is the difference between a security posture that looks good on paper and one that actually holds up under pressure.
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