There is a new model with the arrival of asset tokenization. It is the process of creating digital tokens that represent rights, ownership interests or claims to economic value that are associated with real-world assets. These tokens can facilitate fractional participation, digital records and better transfers depending on the structure.

As blockchain technology advances, the tokenization of assets may revolutionize how people and businesses do business with traditionally illiquid assets that are hard to access.

What Is Asset Tokenization?

The process of creating the digital copy of certificates of rights or interests for a real-world asset is called asset tokenization. The underlying asset could be a property, commodity, artwork, investment fund, or another type of valuable asset.

The tokens are typically recorded on a blockchain. This makes it possible to have a digital trace of transactions and token ownership. There are also some pre-defined activities in relation to the tokens that can be automated by smart contracts.

It is important to note that while tokenizing the format can be used to represent the physical asset, that does not necessarily provide direct legal ownership of the physical asset. What rights the token holder actually acquires is determined by the legal structure. This right may be by way of ownership, equity in the company, accrual rights, or another contractual relationship.

How Traditional Asset Ownership Works

Building manual rights are frequently given in a series of administrative processes. The ownership of any property may involve legal documents, government records, financial institutions and brokers and other intermediaries.

Complications may arise if an asset must be shared among multiple participants. Other assets, as well, have high initial investments, making them inaccessible for those with smaller budgets.

Additional documentation, verification, settlement, fees may apply to transfers. All these factors can limit the liquidity of real-world assets as compared to digital financial assets.

The goal of tokenization is to modernize some such processes by digitizing what are traditionally defined asset-related rights.

Also Read: Top SonarQube Alternatives for Enterprise SAST in 2026

How Tokenization Can Change Asset Ownership

Fractional Ownership

Fractional participation is one of the often-mentioned aspects of tokenization. A valuable asset may be breakable and able to be split up into smaller digital components.

The number of tokens that represent a property may be the number of interests that are defined in that underlying property. The asset may not be held by one participant, but others have smaller interests in the asset.

This may reduce the barriers for participation in relation to some assets. The rights of fractional ownership, however, will be contingent on its legal structure, regulatory requirements, and the rights that come with the tokens.

Digital Ownership Records

Blockchain technology can help to create a common digital ledger of token transaction history. A blockchain can, unlike existing record-keeping systems, record transactions following a codified set of rules of the network.

This can help to create greater transparency and better trackability of ownership records. This system’s effectiveness still depends on how accurate the information that is linked to the blockchain is.

More Efficient Transfers

Interests can be transferable via digital systems. This may make transfer of assets simpler for some administrative processes.

But not all transfers are unrestricted, nor is digital transfer. There may be identity verification requirements, investor eligibility for regulatory requirements and platform rules.

Programmable Ownership

Smart contracts can add “programmable” elements to tokenized assets. They are able to perform pre-stored programs at certain conditions.

For instance, a smart contract might be capable of a lot of automation in distribution, restriction of transfers, or dictating transaction conditions. This can minimize manual work in appropriate processes.

Which Real-World Assets Can Be Tokenized?

When legal and technical structures are available, tokenization can be used for other assets.

One discussed use case is for real estate, where it may have high valuations and be illiquid. Defined interests may be digitally represented in relation to property related structures through tokenization.

Additionally, precious metals may be linked to tokenization models oriented toward a clear claim or interest related to assets deposited in the holding accounts.

Private credit, investment funds, artwork, collectibles, commodities, infrastructure and energy assets may also be wrapped-up in this context.

The answer is that not all assets could be considered appropriate for tokenization. Legal ownership, valuation, custody, regulation, investors’ demand and market ready-ness should all be taken into account.

Potential Benefits of Tokenized Ownership

Greater Accessibility

Some assets can be tokenized to be split up into smaller digital parts. This can provide greater access to asset classes which are normally only open to participants with a fairly large amount of capital. But the participation is subject to asset structure and applicable regulations.

Improved Transparency

Records of transactions and shifts in tokens’ ownership can be kept consistent via the blockchain. Depending on the design of the platform and blockchain, participants can potentially follow the activity that they are interested in in a shared digital record.

Greater Administrative Efficiency

Ownership and transactions under tokenized structures may be digitized. This can help to minimise manual workload, enhance record keeping and make reconciling various different actors in asset management easier.

Potential Liquidity

Tokenization can pave the way for further possibilities of interest transfer in specific assets. Without denying the need for a compliant secondary market, if one can be established, then the trading of tokenised interests may be more efficient than trading with some traditional asset interests. But just tokenization isn’t enough for liquidity.

Process Automation

Smart contracts can automatically take action when certain conditions are met. They can, for instance, facilitate distributions, transfer rules, or other types of transactions. This might help to minimise manual involvement in appropriate instances.

The Future of Real-World Asset Ownership

Traditional asset ownership with blockchain-based infrastructure could form the future asset ownership. In the years to come, with developing regulatory environments and technology, more companies may consider using technology to provide digital representation of real world asset interests.

But, successful tokenization calls for so much more than just generating a digital token. The underlying asset, rights, compliance, technology and asset management processes must be tied to the model.

Evidence of more institutional involvement and improved regulation may also shape the rate by which tokenized assets will gain to the financial mainstream.

As the tokenization ecosystem develops, white label tokenization platform development may provide businesses with an approach to building customized tokenization infrastructure. The structures on such sites are appropriate for a variety of asset types, regulatory requirements, compliance procedures, and the planned rather than trading with structures.

Conclusion

The asset tokenization concept is reshaping the nature of real-world assets representation, management, and transfer. It could enable fractional participation, greater transparency, automated processes, and more streamlined transfers, thanks to a combination of blockchain records, digital tokens, and smart contracts.

Concurrently, tokenization does not take the place of law or regulatory obligations, or investor safeguards. The adoption of the technology will be key to its longevity if the technology complements existing financial and legal frameworks.

These frameworks are still in development and tokenization is a likely part of the changing model of ownership in a real-world asset.

The post How Asset Tokenization Is Changing the Way Real-World Assets Are Owned appeared first on ITDigest.

This ranking focuses on security-first static application security testing. It does not assume SonarQube must be removed from every code-quality workflow. Many enterprises keep SonarQube for maintainability metrics while adopting a dedicated AppSec platform for SAST. Others prefer to consolidate quality and security in one product. The right choice depends on whether the target is a specialist SAST engine or a broader application security platform.

Aikido Security ranks first because it combines modern SAST with AI-assisted code review, broad AppSec coverage, enterprise controls and a direct route from finding to fix. Semgrep is an excellent focused alternative for customizable, fast code security, while Checkmarx, Veracode, Black Duck and Fortify offer mature enterprise SAST depth for complex programs.

Quick comparison

How we ranked the tools

The order reflects practical fit for the stated enterprise use case. It is not a claim that one product is universally better for every architecture.

• Security-specific detection depth, including inter-file data flow, taint analysis, framework awareness and support for common vulnerability classes.
• Signal quality, triage context, suppression governance and the clarity of remediation guidance.
• Feedback speed and integration across IDEs, pull requests, CI/CD and issue trackers.
• Enterprise controls such as SSO, role-based access, policy gates, auditability, compliance reporting and local or self-managed analysis.
• Platform breadth beyond SAST where it improves prioritization or reduces tool sprawl, without treating breadth as a substitute for code-analysis quality.

The best tools, ranked

1. Aikido Security – Best overall SonarQube alternative for enterprise SAST

Official product page: www.aikido.dev/code/static-code-analysis-sast

Aikido Security is the best overall SonarQube alternative for enterprises that want security-first static analysis within a broader application security platform. Its SAST capability analyzes source code across major languages and development platforms, supports custom rules and fits into IDE, pull-request and CI/CD workflows. Aikido also adds AI Code Quality and AI Code Analysis for logic, authorization and business-context issues that deterministic SAST may not capture alone.

The platform’s enterprise value extends beyond the engine. Security teams can govern policies, release gates, access, audit history and compliance reporting across a portfolio, while local scanning options support strict source-code and data-residency requirements. Native SCA, secrets, IaC, container, DAST, API and cloud security add context and reduce the need to operate SonarQube beside a separate scanner for every AppSec category.

Why it stands out

• Security-focused SAST combined with AI code review and broader native AppSec coverage.
• Enterprise SSO, user administration, RBAC, policy gates, audit and compliance controls.
• Local scanning and support for GitHub, GitLab, Bitbucket and Azure DevOps estates.
• Developer remediation through PRs, CI/CD, IDEs and integrated issue workflows.

Best for: Enterprises that want to move from code-quality-led security to a unified, governed application security platform.

Considerations: Teams that rely on highly specialized SonarQube quality profiles or exhaustive technical-debt metrics should map those quality requirements separately. Validate priority languages, custom rules and the largest repositories in a proof of concept.

2. Semgrep – Best for fast, customizable developer-first SAST

Official product page: semgrep.dev/products/semgrep-code

Semgrep is one of the strongest focused SonarQube alternatives for modern enterprise SAST. It combines fast static analysis with an accessible rule syntax, data-flow analysis, centralized policy and pull-request feedback. Security engineering teams can build and distribute organization-specific checks without the operational model of a traditional heavyweight SAST platform.

The commercial AppSec platform adds software supply chain and secrets coverage, AI-assisted triage and enterprise administration around Semgrep Code. Semgrep is especially compelling when custom guardrails, developer speed and security-team extensibility are more important than broad code-quality dashboards or native DAST and cloud security.

Why it stands out

• Fast scans and strong developer workflow.
• Readable, extensible rules for custom security policies.
• Modern data-flow analysis with centralized AppSec management.

Best for: Enterprises that want a programmable, high-speed SAST platform operated closely by security engineering.

Considerations: Broader code quality, DAST, API, container and cloud posture requirements may need additional products. Custom rule programs require governance, testing and maintenance.

3. Checkmarx One – Best for complex, policy-heavy enterprise SAST

Official product page: checkmarx.com/product/application-security-platform/

Checkmarx One is a mature security-first alternative for enterprises that need deep SAST, broad language coverage and centralized policy across a large application estate. The platform also includes SCA, secrets, IaC, API and posture capabilities, allowing security teams to build a formal multi-discipline AppSec program around the static-analysis engine.

Checkmarx is particularly relevant for regulated organizations, legacy codebases and portfolios that require extensive customization. Its depth and flexibility can come with more implementation and tuning effort than developer-first tools. Buyers moving from SonarQube should test both security yield and the impact on build and developer workflows.

Why it stands out

• Mature enterprise SAST and broad technology support.
• Centralized policy, reporting and application governance.
• Strong fit for regulated and heterogeneous portfolios.

Best for: Large enterprises with dedicated AppSec teams, complex policies and legacy as well as modern code.

Considerations: Plan for ruleset tuning, implementation and developer enablement. The platform may be more capability than teams need when the requirement is a focused, easily operated SAST replacement.

4. Veracode – Best for centralized SaaS SAST and assurance

Official product page: www.veracode.com/products/binary-static-analysis-sast/

Veracode is a long-standing enterprise SAST alternative with a SaaS operating model, centralized policy and mature reporting. It is a strong fit for organizations that want consistent application assurance across business units without maintaining a large self-managed scanner infrastructure.

Veracode also offers dynamic and software composition analysis, making it suitable for broader enterprise testing programs. The platform should be tested for feedback speed, build requirements and developer usability on the organization’s real technology stack. Its strengths are formal governance and assurance rather than serving as a direct replacement for every SonarQube code-quality metric.

Why it stands out

• Mature SaaS static analysis and policy management.
• Strong portfolio reporting and regulated-program fit.
• Broader AST portfolio beyond SAST.

Best for: Enterprises prioritizing centralized assurance, auditability and a mature SaaS delivery model.

Considerations: Validate developer feedback loops, language and build support, and the need for adjacent code-quality tooling. Cloud, IaC and container coverage may involve separate capabilities.

5. Black Duck Polaris – Best for Coverity-based SAST plus enterprise SCA

Official product page: www.blackduck.com/platform.html

Black Duck Polaris combines Coverity static analysis with Black Duck software composition analysis and additional security capabilities in a cloud platform. It is a credible SonarQube alternative for enterprises that want mature SAST and SCA from one established vendor, especially when open-source risk and license management are major requirements.

Polaris can support centralized administration and developer workflows at scale. Existing Coverity or Black Duck customers may find it a natural consolidation path. New buyers should examine product packaging, scan architecture and how posture, DAST and remediation requirements are handled across the wider Black Duck portfolio.

Why it stands out

• Established Coverity SAST and Black Duck SCA engines.
• Enterprise-scale SaaS administration.
• Strong fit for combined code and open-source risk programs.

Best for: Enterprises that want mature static analysis and SCA under a common vendor platform.

Considerations: Clarify entitlements and boundaries between Polaris, Software Risk Manager and individual scanners. Evaluate PR feedback and workflow simplicity against newer platforms.

6. OpenText Fortify – Best for legacy languages and flexible deployment

Official product page: www.opentext.com/products/static-application-security-testing

OpenText Fortify SAST is a strong SonarQube alternative for enterprises with demanding language coverage, extensive legacy code or deployment constraints. It supports cloud and self-managed operating models and is backed by a mature security research and rules ecosystem.

Fortify is most effective in a formal AppSec program with analysts and administrators who can tune rules, manage findings and integrate results into delivery. It offers depth and flexibility, but organizations moving away from SonarQube because of operating burden should compare the full administrative workload and developer experience.

Why it stands out

• Deep enterprise static analysis and broad language coverage.
• Flexible SaaS and on-premises deployment.
• Strong compliance and customization capabilities.

Best for: Regulated enterprises, legacy-heavy portfolios and organizations requiring self-managed SAST.

Considerations: Expect meaningful implementation and tuning. Test incremental scan performance, issue clarity and integration into pull requests and developer tools.

7. Snyk Code – Best for developer-centric cloud-native SAST

Official product page: snyk.io/product/snyk-code/

Snyk Code is a developer-oriented SAST alternative that emphasizes fast analysis, inline explanations and remediation in IDEs and pull requests. It integrates naturally with Snyk’s open-source, container and IaC products, making it attractive to cloud-native enterprises already using the broader platform.

Compared with SonarQube, Snyk Code is more security-centered and less focused on comprehensive maintainability metrics. Enterprise buyers should evaluate the full Snyk package, role and policy model, reporting and total licensing across a large developer population.

Why it stands out

• Fast feedback in IDE and pull-request workflows.
• Strong connection to Snyk’s SCA, container and IaC ecosystem.
• Developer-friendly explanations and remediation guidance.

Best for: Cloud-native enterprises that want developers to own static-analysis remediation within a broader Snyk program.

Considerations: Model total platform licensing and confirm requirements for custom rules, local analysis, legacy languages and non-SAST testing. A separate quality platform may still be useful.

8. GitHub Code Security – Best for GitHub-native enterprise SAST

Official product page: github.com/security/advanced-security

GitHub Code Security provides CodeQL-based code scanning, dependency security workflows and campaign-oriented remediation inside GitHub. It is a logical SonarQube alternative for organizations that have standardized on GitHub Enterprise and want SAST results managed directly in repositories and pull requests.

Native workflow and central GitHub administration are its main advantages. The trade-off is ecosystem dependence: non-GitHub repositories and broader AppSec layers require other tools. Enterprises should also distinguish code-security coverage from the separate secret-protection entitlement and model per-committer pricing at scale.

Why it stands out

• Deep integration with GitHub repositories and developer workflows.
• CodeQL query ecosystem and security campaigns.
• Straightforward administration for GitHub-standardized estates.

Best for: Enterprises operating predominantly on GitHub and prioritizing native code scanning.

Considerations: It is not source-control neutral and does not replace broad DAST, API, cloud or multi-SCM security. Evaluate licensing and CodeQL coverage for the languages in scope.

How to choose the right platform

Define security depth requirements

List the vulnerability classes, frameworks, languages and inter-file data flows the program must detect. Use known internal examples and seeded tests rather than relying on generic benchmark claims.

Compare signal and explanation

Review true positives, false positives, trace clarity, severity rationale, suppressions and fix guidance. The best SAST program is the one developers can understand and act on consistently.

Test the feedback loop

Measure time from commit to result, build impact, PR annotation, IDE support and the number of steps required to create or apply a fix. Slow feedback turns SAST into a late-stage gate.

Separate code quality from application security

Determine whether maintainability, duplication, coverage and technical-debt dashboards must remain in the same product. A dedicated SAST tool may be stronger for security while SonarQube or another quality platform remains in place.

Validate enterprise administration

Test onboarding, policy inheritance, custom rule governance, exceptions, audit evidence, role separation, data handling and reporting across multiple business units.

Frequently asked questions

What is the best SonarQube alternative for enterprise SAST?

Aikido Security is the strongest overall option for enterprises that want security-first SAST, broad native AppSec coverage, enterprise governance, local scanning and developer remediation. Semgrep is a strong focused SAST choice, while Checkmarx, Veracode, Black Duck and Fortify fit mature centralized programs.

Can Aikido replace SonarQube in an enterprise?

Aikido can replace SonarQube for security-oriented static analysis and can also cover code quality, AI code review and broader AppSec categories. Enterprises that rely heavily on SonarQube-specific maintainability metrics or custom quality profiles should map those requirements during the migration.

Should we keep SonarQube for code quality?

Potentially. Some organizations use a dedicated SAST platform for security and retain SonarQube for quality and technical debt. Others consolidate both. The decision should be based on workflow, coverage, policy and administration rather than a preference for one-tool or two-tool architecture.

Which SonarQube alternative supports on-premises or local analysis?

Aikido offers local scanning options, while Fortify and other traditional enterprise platforms provide self-managed deployment choices. Exact data flow and feature parity vary, so security architecture teams should validate the proposed configuration.

Conclusion

The best SonarQube alternative for enterprise SAST is not necessarily the closest code-quality clone. Aikido Security leads because it brings security-first static analysis, AI-assisted review, enterprise governance and broader code-to-cloud coverage into one developer-facing platform. Semgrep offers a focused and highly extensible modern SAST experience. Checkmarx, Veracode, Black Duck and Fortify provide mature enterprise depth, while Snyk and GitHub align strongly with developer ecosystems. The right proof of concept should measure exploitable issues found, noise avoided, time to feedback and fixes completed.

The post Top SonarQube Alternatives for Enterprise SAST in 2026 appeared first on ITDigest.

In this article, we explore why custom software development outperforms off-the-shelf software, and how businesses can benefit from adopting solutions designed specifically for their needs.

What is Custom Software Development?

Custom software development refers to the process of creating software solutions tailored to a company’s specific requirements. Unlike off-the-shelf solutions, which are mass-produced for a wide range of users, custom software is built from the ground up to meet the unique challenges and goals of a business. It involves in-depth collaboration between the development team and the client to design a system that fits the company’s workflows, operations, and objectives.

While off-the-shelf software typically serves a broad audience, custom software development ensures that businesses get exactly what they need, without unnecessary features or limitations.

1. Tailored to Business Needs

The primary benefit of custom software development is that it is designed to meet the exact needs of a business. Off-the-shelf software often comes with a set of pre-built features that may not align perfectly with a company’s operations or workflows. For example, a retail company may require a specific inventory management system that integrates seamlessly with its supply chain, sales data, and customer relationship management (CRM) tools. An off-the-shelf solution may not offer this level of integration or customization, forcing the company to adjust its processes to fit the software.

Custom software development, on the other hand, allows businesses to define their requirements from the start. The development team works closely with the company to ensure the software is built to suit their specific goals and objectives. Whether it’s improving customer engagement, automating tasks, or streamlining internal communication, custom software can be designed to address the unique challenges a business faces.

2. Scalability and Flexibility

As businesses grow, their needs evolve. Off-the-shelf software may struggle to accommodate the changes that come with expansion, such as increased data volume, new features, or additional users. Custom software, however, is designed with scalability in mind. As a business grows, custom software can be easily adjusted and expanded to meet new demands.

Custom software development allows businesses to add new features, integrate with other tools, and enhance the functionality of the system over time. Whether expanding to new locations, entering new markets, or launching new products, custom software can grow alongside the company. In contrast, off-the-shelf software often requires businesses to switch to a more expensive plan or upgrade to a different version, which may not always be as seamless or cost-effective.

3. Integration with Existing Systems

Many businesses already rely on various software systems to manage their operations. Whether it’s customer relationship management (CRM) software, accounting tools, or marketing platforms, integration between these systems is crucial for maintaining efficiency and preventing data silos. Off-the-shelf software often comes with limited integration capabilities, which may not be compatible with the systems a business already uses.

Custom software development ensures that all systems work together seamlessly. Developers can build custom integrations with existing tools, allowing for smooth data transfer between systems and creating a unified software ecosystem. This integration reduces the risk of errors, enhances data accuracy, and eliminates the need for employees to manually transfer data between systems.

4. Security and Data Protection

With the increasing number of data breaches and cyber threats, businesses must prioritize security when choosing software solutions. Off-the-shelf software may be a target for cybercriminals due to its wide usage and the presence of known vulnerabilities. While these solutions often come with basic security measures, they may not be robust enough to meet the specific security requirements of a business, especially when dealing with sensitive customer or financial data.

Custom software development offers a higher level of security. Since custom software is built specifically for a business, developers can implement advanced security protocols tailored to the company’s needs. From data encryption and secure authentication to role-based access control and regular security audits, custom software ensures that sensitive data is well-protected. Additionally, businesses can quickly respond to emerging threats, updating the software to address new vulnerabilities as they arise.

Also Read: Architecting Large Language Models for Marketing Success

5. Cost-Effectiveness in the Long Run

At first glance, off-the-shelf software may appear to be the more cost-effective option due to lower upfront costs. However, over time, businesses often find that off-the-shelf software ends up being more expensive. Hidden costs include licensing fees, subscription renewals, and additional charges for upgrades or advanced features. Additionally, businesses may need to spend extra on third-party tools or services to fill gaps in functionality or integration.

Custom software development may have a higher initial cost, but it can save money in the long run. Since the software is built to meet a business’s exact needs, there are no recurring licensing fees or charges for unused features. Furthermore, custom software eliminates the need for third-party tools or services, as everything is integrated into a single solution. The software also reduces operational costs by automating processes and improving overall efficiency.

6. Faster Adaptation to Market Changes

The business world is constantly evolving, and companies need to stay ahead of the curve to remain competitive. Off-the-shelf software is often updated infrequently, and businesses may have to wait for a vendor to release new features or address issues. In contrast, custom software development offers businesses the flexibility to adapt quickly to market changes.

When a business needs to add new features, update processes, or implement regulatory changes, custom software can be modified and deployed quickly. Developers can work closely with the company to ensure that any changes are implemented seamlessly, without causing disruptions. This level of responsiveness is invaluable in industries that are rapidly evolving or facing shifting consumer demands.

7. Superior Customer Support

When businesses rely on off-the-shelf software, customer support can often be frustrating. Support teams are usually overloaded, and businesses may have to wait for extended periods to receive assistance. Additionally, off-the-shelf software vendors may not always offer the level of support needed to address specific issues or customization requests.

With custom software development, businesses have access to dedicated support from the development team. Whether it’s troubleshooting, software updates, or additional feature development, a dedicated team is available to provide timely assistance. This ensures that businesses can continue to operate smoothly without having to wait for a generic support team to address their needs.

Conclusion

While off-the-shelf software may seem like a quick and easy solution, it often falls short when it comes to meeting the unique needs of a business. Custom software development offers a range of benefits that make it the superior choice for businesses looking for tailored, scalable, and secure software solutions. From enhancing customer engagement and improving internal operations to ensuring long-term cost savings and adaptability, custom software provides a strategic advantage that off-the-shelf solutions simply cannot match.

By working with a skilled development team, businesses can create software that is perfectly aligned with their goals, helping them remain competitive in an ever-changing market. Custom software development may require a larger initial investment, but the benefits it provides in terms of flexibility, security, and long-term value make it an investment worth considering for any forward-thinking business.

The post Why Custom Software Development Beats Off-the-Shelf Solutions appeared first on ITDigest.

Model-as-a-Service (MaaS) via Application Programming Interface (API)

The first approach and most straightforward is to outsource. Marketing teams can leverage pre-trained LLMs through cloud-based APIs by the likes of GCP, Azure, and AWS. Marketers can take advantage of the power of LLMs without the complexity of requiring their enterprise IT teams to support them. The service provider maintains the models, ensures smooth functioning, and takes care of updates.

The MaaS approach provides a quick and easy way to use powerful language processing features in their marketing initiatives. Chatbots, content generation, and sentiment analysis designed with LLMs can keep marketing teams ahead of the competition. Further, marketing teams can gain
valuable insights from customer feedback, social media conversations, and consumer trends, making it simpler to inform marketing strategies and identify emerging opportunities.

MaaS has advantages, yet limitations exist. One of the most important is the dependence on the provider for appropriate data handling practices. Incorrectly managed data can compromise data residency and privacy, putting organisations at risk. Cost can also be an issue as usage increases.
MaaS offers flexible pricing but API providers charge by the number of queries and quantity of text submitted. This flexibility can turn a well performing experiment into an expensive mistake if not monitored. Marketers must also assess and accept the risk that network and system issues can interrupt use and the provider could go out of business or shutter the service.

Also Read: The 3 Stages of the Data Privacy Maturity Curve

Managing Open Source LLMs in an Organisation’s Environment

The second approach marketers (and businesses) can pursue is to download and run an Open Source LLM within their own environment from platforms like huggingface.co. These platforms aggregate a wide range of general and domain-specific models, enabling companies to choose
what best suits their needs without being limited by vendor lock-in. Open Source models allow marketers to explore and experiment with new advancements and customise them to their specific needs, such as industry, target audience, or brand identity. Also, self-management can be a
cost-effective alternative for small marketing teams with limited budgets. But, there is a compromise to self-managing Open Source LLMs; most organisations won’t be able to do so with their in-house engineering teams and will most likely require outside help. Getting started with an LLM can be straightforward but maintaining an LLM requires expertise in many areas, like data science, machine learning, NLP, and engineering. Further, LLM model tuning requires architecture, programming language and training procedure understanding, all skills in short supply. Also, Open Source models can lack contextual understanding and the extent of customisation is likely to be limited, as communities for Open Source models are smaller and more focused on their application, unlike the unbound choices of MaaS.

Choosing the right approach

Given the tradeoffs between the different approaches, how can an organisation be sure to choose the one that’s right for them?
There is no one-size-fits-all approach that will be appropriate for marketing teams or the whole enterprise. The better option is for organisations to team up with the right solutions partner — one that offers expertise in data science, machine learning, and NLP — to assist them in selecting the
most suitable model and architecture for their desired applications or use cases. With expert support, enterprises can build the requisite broader framework necessary for deploying any LLM or AI use case and harness their magic for their marketing strategies or overall operations. Through building the right foundation, businesses will then have the flexibility to deploy their chosen LLM model with key features that include data connectors, data preparation capabilities, automation, and governance — all the right ingredients to ensure LLM wizardry can continue to
revolutionise marketing strategies.

The post Architecting Large Language Models for Marketing Success appeared first on ITDigest.

As compliance regulations evolve through the years from establishment to enforcement, businesses also follow a path of implementing the bare minimal compliance to conform to the text of the law. Most regulations also mature over time as they adapt to market realities. For example, PCI-DSS v1.0 evolved and matured over the years since its inception in 2006 to its current iteration (PCI-DSS v3.2.1) to protect cardholder data and reduce fraud. The same pattern in data privacy compliance should follow as the California Privacy Protection Agency (CPPA) begins enforcing the California Consumer Privacy Act (CCPA) more actively. 

Recently, businesses got a first look into what CCPA enforcement may look like when beauty retailer Sephora was fined $1.2 million for selling consumers’ personal information (PI) without their consent. History tells us that active enforcement and penalties, like we have seen with the General Data Protection Regulation (GDPR), HIPAA/HITECH, PCI-DSS and other compliance regulations over the years, are likely to accelerate businesses’ commitment to comply with data privacy compliance. But it will take time. 

According to research conducted by CYTRIO during Q2 2022, 91% of companies are not prepared for CCPA privacy compliance as enforcement begins to take effect. Most companies are still at the starting gate of where to begin with data privacy, doing nothing or having the bare minimum in place.

Also Read: How is Anomaly Detection A Shield of Security for Multiple Industries?

While many companies are just starting in their data privacy journey, it’s likely that most will follow a 3-stage maturity curve (figure 1) we’ve seen with other compliance regulations that have come before. Here are the stages that companies are most likely to navigate through in their data privacy compliance journey:

1. Established Stage

Most companies have been collecting personal information from consumers and employees for years and need to start with understanding what this data is. What personal information does the company have? For what purpose is the information being collected? Where is it located? How is the information being shared? Conduct a Data Privacy Impact Assessment to identify risks arising out of the processing of personal information and to minimize these risks. Create a risk map of where the risk exposure is in collecting, storing, using, sharing, and processing personal information.

Often managed by marketing teams, four outward facing components of outward facing controls need to be put in place during this stage: Notice and Policy, Consent Preference Management, Cookie Policy and Management, and Data Subject Access Request (DSAR) Portal for end users to easily submit data requests. Companies need to develop a well-defined Privacy Notice and Privacy Policy that clearly articulates and informs both internal and external stakeholders on what the company’s position is as it relates to CCPA and other applicable data privacy regulations. 

DSAR Portal provides a mechanism for consumers to exercise their data privacy rights. Consent and Preference Management makes sure you are receiving consent from the consumer at the time you’re collecting their personal data. As part of Cookie Policy and Management, the company needs to disclose what information they are collecting and what cookies are used by the company. 

You also need the ability to understand data mapping, and where and how data is mapped. In this phase, businesses are moving away from using Excel spreadsheets and other manual processes to a more efficient, effective automated solution. Understanding the impact that data privacy has on your business becomes less manual and time intensive with automation. Businesses are also continuously ensuring stored data meets the requirements of various applicable data privacy regulations.

In CYTRIO’s research, nearly 45% of companies are in the Established Stage which is relatively easy for most companies to achieve. Most companies will start their data privacy compliance journey with the Established Stage and may take 6-12 months to move to the Maintain Stage.   

2. Maintain Stage

Once processes are in place for understanding what data is being collected, how it is stored, used, processed and shared, risk exposure is understood and notices and consents are in place, the next step is maintaining what has been established. How do you make sure you are maintaining what has been put in place?

Companies need a mechanism to continuously analyze, measure, and report on how your privacy program is working and responding to end user data requests while continually reducing data privacy risk exposure. Companies must think about connecting front end privacy controls with their marketing stack to make sure they are honoring end user consent and preferences. Implement a self-service portal to allow end users to manage their preferences. 

As DSARs pick up in volume and frequency, companies must look to implement DSAR response automation solutions that can validate, request or identify, automatically discover all PI data, provide response workflows, and share data securely in response to a data request. These automation tools will reduce the cost of DSAR response from approximately $1,400/request to less than $200-300 per request.

3. Evolved Stage 

This stage helps put mechanisms in place to proactively develop and incorporate data privacy principles in all aspects of your company. If you’re building applications that are collecting personal information, DevOps Privacy Engineering (shift-left privacy) builds controls upfront to comply with data collection and consent mechanisms, making sure that ultimately, as the application collects data, it will be used for the right purposes. The Evolved Stage includes data governance, data masking, and data retention to make sure the right people and processes are accessing PI data for the right purpose, and data is not being mis-used and deleted beyond stated purpose.

In the data privacy compliance journey, the Evolved Stage is likely years away. Businesses cannot successfully implement the Evolved Stage or even the Maintain Stage without successfully and comprehensively implementing the Established Stage first. Don’t know where to start? Start with a well thought out privacy program which includes understanding your privacy risk exposure, what PI data you have, where it is, and how it is being stored, processed, shared, and used.

As California Privacy Rights Act (CPRA) goes into effect on January 1, 2023 with expanded rights for consumers and employees, we’ll not only see companies establishing privacy policies, we’ll start to see companies move along the maturity curve. Ultimately, that will result in better privacy and protection for consumers.

The post The 3 Stages of the Data Privacy Maturity Curve appeared first on ITDigest.

Optimistic predictions claim that 2022 will be the year that programmatic increases its penetration of US DOOH to 15%, with a value of $367m. The channel has seen the adoption of yield managers, also known as Header Bidding, as a turnkey solution to tackle these challenges.

Looking elsewhere in the digital advertising landscape, Header Bidding has shown its worth, with more than half (55%) of publishers having leveraged programmatic yield optimization to sell video inventory for Connected TV (CTV). Its ability to both streamline media trading for publishers and give advertisers greater transparency is just the tip of the iceberg when it comes to why the technology has become a mainstay of online programmatic.

Unlocking more revenue

Put simply, Header Bidding is an advanced method of programmatic trading that allows publishers to offer their inventory to multiple SSPs using a unified auction framework. This allows publishers to invite multiple demand sources to bid simultaneously on available inventory.

Also Read: Billups Acquires Ad Tech Startup Outchart to Advance Programmatic Digital Out-of-Home (OOH) Aspirations

When compared to the traditional waterfall auction method, where inventory is offered to one ad network at a time before it is moved on if the floor price isn’t met, this upgraded method prevents publishers from missing out on revenue because of the lack of competition for ad impressions.

In the digital space, media owners have reported significant revenue growth when using Heading Bidding, with one example of up to 70%. Seeing the benefits it brought to display, mobile and audio, DOOH publishers can only expect comparable revenue lifts.

More control over demand

A yield manager provides publishers greater control over which demand partners are included in the auctions and lets them be more selective. By having more visibility on the incoming bids, resources can therefore be managed more efficiently during the bidding process which means that advertisers will be served quality promotions, furthering the value of their inventory.

More efficiency

For DOOH media owners, a yield manager enables SSPs to be managed without the need to build their own mediation layers. Though only a relatively recent development in the programmatic DOOH space, the yield manager is integrated into an Ad Server, enabling publishers to benefit from a first-price, parallelized, and unified auction that maximizes revenue without having to hire a team of developers.

More convenience

Ultimately, Header Bidding solutions are easy to manage. Activity can be viewed in a unified dashboard. For programmatic DOOH, where many screens may be being targeted simultaneously, across multiple locations, or even across multiple countries, gaining a streamlined overview of biddings in real-time eases the process and brings convenience. Working with one auction entry also drastically reduces the complexity and the reporting discrepancies.

As the adoption of programmatic DOOH continues to grow, yield optimization solutions will allow the channel to flourish even further. The promise of increased revenue, control over demand, efficiency and convenience will likely lead to yield managers being widely embraced by media owners.

The post Why Adopting a Programmatic ‘Digital Out Of Home (DOOH) Yield Manager’ is Key to Drive Publisher Growth appeared first on ITDigest.

Earlier this summer, Gartner predicted public cloud services would grow 19 percent just this year, despite also acknowledging the impacts of COVID-19 would cause overall global IT spending to decline eight percent in 2020. With this rapid growth, companies making quick transitions to the cloud can overlook or miss key learnings and vital steps that can guarantee a successful cloud migration. One coding error can have a significant impact on application performance. For these transitions, AI for Operations, or AIOps, can be an essential tool in helping avoid such mistakes.

In our recent AppDynamics  Agents of Transformation Report, it was revealed that 95 percent of organizations have changed their technology priorities and pivoted their strategy during the course of the COVID-19 pandemic. Much of this change is driving more businesses to the cloud, and if a company does not fully understand their new cloud platform or if they make a small error, the end-user can have a negative experience.

AIOps is key to supporting a successful cloud migration strategy 

By providing technologists insights into uptime, performance, and availability, AIOps helps identify and solve issues before they can affect the end-user during cloud migration. AIOps also helps provide visibility within multi-cloud, and containerized environments as organizations manage more complex platforms.

IT environments in the cloud can support the influx of data. Still, with several applications being added, deleted, or continuously changed, it is a complex process to manage without having cross-platform visibility. AIOps provides that overview while also giving rapid updates and suggestions for optimization, helping businesses gain a competitive advantage and control in the multi-cloud world.

Where does AIOps fit in your cloud migration strategy?

AIOps is designed to tackle challenges with IT complexity, but where should you begin implementing AIOps during the cloud migration process? There is a simple methodology company can use before and after their cloud migration.

When you decide to make a move and right before you begin the migration process, the first components to measure are your existing performance baselines. You have the opportunity to define performance goals and identify the current bottlenecks you wish to avoid when migrating to the cloud. It is also essential to evaluate the potential impact of your cloud migration on your IT environment, which can be managed by AIOps. With full visibility across inter-app dependencies, you can be aware of results on the apps powering your business.

Once your IT environment is migrated to the cloud, you will want to consider where AIOps needs to be used, paying close attention to which resources have a possibility of being underutilized. The use of AIOps on the cloud will also provide insight on over-provisioned resources, allowing you to observe and identify instances better suited for your company’s needs.

Having the ability to monitor your service on the cloud independently and the end-user performance also gives you full visibility into what you are getting from your cloud provider. Knowing that demands can change, a cloud migration strategy with AIOps is also crucial for identifying the areas that need improvement, so you know what applications might benefit from being moved. It’s about finding the right opportunities at the right time.

To help your company thrive after a successful cloud migration, there needs to be more than just cloud integrations in place. Such an infrastructure requires automation skills, observability, and analysis practices along with a deep understanding of the techniques that can reduce application downtime. The reliance on cloud support is only continuing to increase, so the sooner you take control of your IT environment with AIOps, the sooner you can have confidence your services will deliver improved performance for the end-user. Why not get the most out of your investment in the cloud by using tools like AIOps to support your business for the long term?

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But that’s not to say that the pandemic hasn’t had an enormous impact on the digital transformation plans of organizations in different industries all over the world. Recent Nuxeo research with software developers revealed that for 70% of companies, digital transformation has become more of a priority​ since the pandemic first emerged.

The digital transformation imperative has intensified and accelerated in 2020, as the pandemic has disrupted entire markets and normal business practices. As new waves of the pandemic compound the impact of original lock-downs and cement more permanent changes to the way the world works, organizations have found that they now have no choice but to reinvent their established practices and client services.

As organizations strive to make these changes, the focus is on how best to approach doing so. Low-code development tools have grown in both prominence and use in 2020 and are regarded as increasingly important in accelerating digital transformation pace.

Changing business requirements

Some companies had already lost a great deal in 2020 – revenue, customers, market position – because they weren’t ready or didn’t have a plan B when circumstances called for it.

That new digital service, channel, or app they were devising: if it wasn’t primed to go live, it is likely to have been put on pause as businesses redirected energy and resources to fire-fighting and trying to survive the immediate crisis brought on by the pandemic.

But now, it has become clear that companies and their customers are in this for the long haul, which means organizations must adapt and innovate today if they still want to survive.

This, in turn, is putting pressure on software development teams to move quickly: they need to find ways to deliver new digital capabilities and services in a matter of weeks rather than months or years.

This is where low-code development tools have proved to be so effective. Low-code tooling makes developers more efficient by reusing existing components and templates to speed up application delivery.

In the context of content-based applications, it introduces the ability to create a new digital customer or supply-chain experience with a very rapid turnaround. In short, low-code allows IT teams to make smarter and more efficient use of their time and skills, accelerating the delivery of new user experiences.

The impact of low-code tools

Nuxeo’s research shows that digital transformation has become more of a priority for seven in every ten organizations since the threat of COVID-19 first emerged. In turn, this has been having a direct effect on development teams and what is expected of them.

When asked to identify the main implications for their role, 37% of software developers confirmed they were under renewed pressure to enable more effective collaboration across their organizations as people continued to work from home.

A third (32%) were being called upon specifically to facilitate more effective use of content while teams were dispersed and to enable new experiences and services, while a similar number (31%) felt under intensified pressure to launch applications at speed.

It is no coincidence that the profile of, and interest in, low-code is rising. Close to a half of software developers said they already used low-code development tools occasionally (once a week or less), and almost two-thirds (64%) reported that their use of low-code tools had increased in 2020 (since the initial global lockdown).

Demand is coming from employers, too, as the benefits of low-code become more widely appreciated. More than half (55%) of developers in the survey said they were being encouraged by the business to use low-code development tools. A substantial majority (60%) reported that they use these tools specifically to create content-based applications.

The low-code benefits

Nuxeo respondents cited some of the benefits of low-code development tools to the business, and seeing those, it is little surprise to see how the use of low-code tools has grown. The principal benefits included simplifying the development process (cited by 35%); accelerating digital transformation (by 25%), and boosting innovation (also cited by 25%).

Tools that speed the development process are hugely impactful in terms of helping companies transform and innovate, which has a knock-on effect in improving the customer experience. This all helps make a business more competitive – always important but especially so in this current period of uncertainty.

It is important to note that COVID-19 didn’t create the need for businesses to digitally transform; it’s just one more contributing factor. But if it was necessary to be agile, create new services and experiences to respond to the market demand quickly before, then it has now become essential. COVID-19 didn’t establish the need for this but did create a real sense of urgency.

Low-code tools can significantly enable addressing this urgency. Their use is all about being ready for tomorrow and what could happen in an always evolving environment.

In simple terms, they accelerate digital transformation and the businesses that adapt to the use of low-code tools will find themselves infinitely better placed for the future than those that do not.

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