Technology has been a passion of mine for a long time, so I have personally found it fascinating to watch the variety of opinions and perspectives unfold as technologies mature. It has challenged my thinking and preconceptions, and I recognize that as a leader and a human I need to address them.

I’ve covered everything from fundamental ethics and whether it is good or bad, through to more specific considerations like ‘what do I want from AI?’, and therefore ‘what might others want from it?’.

Naturally, it’s been hotly debated by my colleagues. The leadership team is considering how today’s and tomorrow’s versions of AI should shape our own role in AI creation and adoption.

We already know that artificial intelligence for IT operations (AIOps) is poised to take advantage of the benefits. At Avantra, we’ve long evangelized the value of automation to offset risk, better utilize skill and boost productivity and innovation. But the debate that surrounds AI has helped us understand that our next phase of technical development must be underpinned with even greater pragmatism and responsibility.

The latest numbers I saw suggest that every day, 100 million people are experimenting with the likes of ChatGPT and other Large Language Models (LLM), such as Bard. Numbers like this highlight the popularity and allure that machines still have. Just like the washing machine, if it makes life easier, why would you not use it?

Download: ChatGPT and Generative AI in the Workplace

Proceeding with Caution

However, even though world renowned university colleges are condoning the use of conversational AI tools, leading experts are urging caution — legalities, politics, economics and ethics are top of the list of concerns.

It was eloquently summed up by The Future Life Institute, which is made up of over 1,000 experts, in an open letter asking the industry to pause AI development, or risk humanity and society. LLMs are learning so much so fast, that we, as a species, haven’t had time to truly process the long term impact. Ethics are at stake.

Taking Responsibility is Urgent

I think it’s a responsible challenge. As I said before the headlines have provoked my own thinking to evolve and prompted me to consider whether such a warning could, should or even will stop our own industry from forging on.

This is where I think the application of AI must be balanced against the dilemma. Take the example of producing project documentation or new product technical summaries. Is using ChatGPT to create the first draft irresponsible or a boon for productivity, freeing up time for innovation in other areas? Similarly, with the introduction of ‘copilot’ tools, like Microsoft assistant, people can increase their productivity and have more time for other things, even just going to the gym. I can see how it could make a sustainable argument for a four day week and happier colleagues.

Can Conversational AI Help Our Industry?

Of course, in my world, the real advantage of introducing ML and AI is the ability to help customers find answers to the problems they face. Using conversational AI to mine a database of known and defined errors other businesses have encountered — be that on SAP or Google — would help practitioners arrive at answers far sooner and avoid a degradation in productivity.

The process would augment the value of the intelligence we aggregate and own and, as it’s a trusted source, accelerate decision making and the time to resolution (TTR). No human can realistically (nor would they want to) hold in their brain all the common problem scenarios and fixes.

I’ve tried to do this in my professional career, and though possible, it is exhausting. That’s why I believe, applying conversational AI to the common challenges our customers face would help highly qualified and skilled humans validate and implement the decisions they take.

I’d advocate that automating the interrogation of vast knowledge banks makes complete sense, especially when it helps skilled people get on with doing what they do best — running, managing, and developing world class ERP.

I should be clear that I am wedded to the notion that it’s important the wider industry runs the AI race in tandem with the ethics that protect humanity. We must thoroughly understand the implications at every point in development and put in place the checks, balances and regulation to ensure the values we hold dear are protected and enhanced, not obliterated.

In the world of AIOps there is real value to its adoption not least to ensure mission critical systems related to food supply or energy stay online. We must therefore consider the broad view of AI technology as well as our narrower domain. Only with a balanced view and appreciation of the accountability we assume as leaders, can we make the right choices.

John Appleby leads Avantra as the Chief Executive Officer. Before Avantra John served as the Global Head of DDM/HANA Center of Excellence at SAP and as the Global Head of SAP HANA solutions at Bluefin Solutions, subsequently acquired by Mindtree. John is a recognized thought leader in the SAP market and was part of SAP’s Mentors Group. John holds an MA in computer science from the University of Cambridge.

The post Does Conversational AI Have A Role to Play in AIOps? appeared first on My TechDecisions.

“The need to deliver provable value to the organization at scale is driving these trends in D&A,” said Gareth Herschel, VP Analyst at Gartner, in a statement. “Chief data and analytics officers (CDAOs) and D&A leaders must engage with their organizations’ stakeholders to understand the best approach to drive D&A adoption. This means more and better analysis and insights, taking human psychology and values into account.”

Gartner analysts presented the top 10 D&A trends that business and IT leaders must engage and incorporate into their D&A strategy at the 2023 Gartner Data & Analytics Summit.

Trend 1: Value Optimization

Most D&A leaders struggle to articulate the value they deliver for the organization in business terms. To achieve value optimization from an organization’s data and artificial intelligence (AI) portfolio, specific competencies such as value storytelling, value stream analysis, investment ranking and measuring business outcomes are required. D&A leaders should build clear links between their projects and the organization’s mission-critical priorities.

Trend 2: Managing AI Risk

The growing use of AI has exposed companies to new risks such as ethical risks. Managing AI risks is not only about being compliant with regulations. Effective AI governance and responsible AI practices are also critical to building trust among stakeholders and catalyzing AI adoption and use.

Trend 3: Observability

Observability is a characteristic that allows the D&A system’s behavior to be understood and allows questions about their behavior to be answered.

“Observability enables organizations to reduce the time it takes to identify the root cause of performance-impacting problems and make timely, cost-effective business decisions using reliable and accurate data,” said Herschel. “D&A leaders need to evaluate data observability tools to understand the needs of the primary users and determine how the tools fit into the overall enterprise ecosystem.”

Trend 4: Data Sharing Is Essential

Data sharing includes sharing data both internally (between or among departments or across subsidiaries) and externally (between or among parties outside the ownership and control of your organization). Organizations can create “data as a product,” where D&A assets are prepared as a deliverable or shared product.

“Data sharing collaborations, including those external to an organization, increase data sharing value by adding reusable, previously created data assets,” said Kevin Gabbard, senior director, analyst at Gartner, in a statement. “Adopt a data fabric design to enable a single architecture for data sharing across heterogeneous internal and external data sources.”

Trend 5: D&A Sustainability

According to Gartner, it is not enough for D&A leaders to provide analysis and insights for enterprise ESG (environmental, social, and governance) projects. D&A leaders must optimize their own processes for sustainability improvement. D&A and AI practitioners are becoming more aware of their growing energy footprint. As a result, a variety of practices are emerging, such as the use of renewable energy by (cloud) data centers, the use of more energy-efficient hardware, and the usage of small data and other machine learning (ML) techniques.

Trend 6: Practical Data Fabric

Trend 7: Emergent AI

ChatGPT and generative AI are the vanguard of the coming emergent AI trend. Emergent AI will change how most companies operate in terms of scalability, versatility and adaptability. The next wave of AI will enable organizations to apply AI in situations where it is not feasible today, making AI ever more pervasive and valuable.

Trend 8: Converged and Composable Ecosystems

Converged D&A ecosystems design and deploy the D&A platform to operate and function cohesively through seamless integrations, governance and technical interoperability. An ecosystem’s composability is delivered by architecting, assembling and deploying configurable applications and services.

With the right architecture D&A systems can be more modular, adaptable and flexible to scale dynamically and be more streamlined to meet the growing and changing business needs and enable evolution as the business and operating environment inevitably change.

Trend 9: Consumers Become Creators

The percentage of time users spend in predefined dashboards will be replaced by conversational, dynamic and embedded user experiences that address specific content consumers’ point-in-time needs.

Organizations can expand the adoption and impact of analytics by giving content consumers easy to use automated and embedded insights and conversational experiences they need to become content creators.

Trend 10: Humans Remain the Key Decision Makers

Not every decision can or should be automated. D&A groups are explicitly addressing decision support and the human role in automated and augmented decision making.

“Efforts to drive decision automation without considering the human role in decisions will result in a data-driven organization without conscience or consistent purpose,” said Herschel. “Organizations’ data literacy programs need to emphasize combining data and analytics with human decision-making.”

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The post Gartner’s Top 10 Data & Analytics Trends for 2023 appeared first on My TechDecisions.

According to the company, the new innovations further enhance its threat and information protection platforms, in addition to its newly formed Identity Threat Defense business (formerly known as Illusive), to help organizations augment and safeguard their productivity investments, such as Microsoft 365, with maximum deployment flexibility.

“Proofpoint continues to deliver on innovations that empower organizations to break the attack chain,” said Ryan Kalember, executive vice president, cyber security strategy, Proofpoint in a statement. “By providing our customers a unified path to solve for risk across email, cloud, identity and data, CISOs gain unparalleled visibility into and protection against the tactics that attackers rely on most.”

Proofpoint’s Aegis Threat Protection Platform

Proofpoint Aegis Threat Protection Platform is an AI/ML-powered threat protection platform that disarms attacks, such as business email compromise (BEC), phishing, ransomware, supply chain threats. With flexible deployment options using both APIs and inline architecture, Aegis delivers AI-powered, cloud-based protection that complements native Microsoft 365 defenses, says Proofpoint.

By combining the company’s proprietary behavioral analytics and threat intelligence, Proofpoint is delivering new capabilities that provide visibility into account takeover-based attacks from both within an organization’s environment and outside suppliers.

Supplier Threat Protection

Supplier relationships are a growing attack vector: 69% of organizations experienced a supply chain attack within the past year, and CISOs rate it as one of their top concerns, according to Proofpoint research. With Proofpoint’s Supplier Threat Protection, organizations can detect compromised supplier accounts so that security teams can swiftly investigate and remediate.

This new product proactively monitors for and prioritizes known compromised third-party accounts, simplifies investigation with details on why the account is suspected compromised and which employees recently communicated with the account in question, enabling security teams to seamlessly defend against prevalent third-party attacks such as BEC and phishing.

Targeted Attack Prevention Account Takeover (TAP ATO)

Threat actors successfully override MFA in 30% of all targeted cloud and email account takeover attacks according to Proofpoint threat research. Once inside, malicious actors can hide undetected in an organization’s environment, waging sophisticated attacks at will.

Proofpoint TAP ATO, available at the end of Q2 2023, provides visibility across the entire email account takeover attack chain. It accelerates response investigation and remediates accounts, malicious mailbox rule changes, and manipulations of third-party apps and data exfiltration across email and cloud environments.

Identity Threat Defense (formerly known as Illusive)

From ransomware to APTs, 90% of attacks rely on compromised identities, says Proofpoint. The complexity of managing Active Directory (AD) has resulted in the presence of exploitable privileged identity risks in all organizations at a rate of one in six endpoints.

These identity risks include unmanaged local admins with stale passwords, misconfigured users with unnecessary privileges, cached credentials left exposed on endpoints and much more. When an attacker compromises an endpoint with these privileged identity risks, deploying malicious software and stealing data is easy. Privileged identities represent the keys to the kingdom, which attackers exploit to steal the crown jewels. Unfortunately, most organizations are unaware of this risk – until they are attacked.

Leveraging new advanced identity risk analytics and automated detection, Proofpoint has further bolstered its Identity Threat Defense platform – undefeated in more than 150 red team exercises – to provide organizations with comprehensive identity risk protection and remediation:

 Spotlight Risk Analytics

The new advanced risk analytics in the Spotlight dashboard allows users to gain an executive view of an organization’s risk trends as well as exposure across various risk categories and risk exposure levels. It also provides recommendations for possible user admin action.

Spotlight Risk Analytics simplifies decision makers’ workload while ensuring organizational leaders can make informed decisions to remediate modern and sophisticated identity risks. With availability expected late Q2 2023, decision makers will also be able to follow risk trends to track their organization’s risk posture improvements over time.

Proofpoint Spotlight Cross Domain & Trust Visibility

For organizations with complex infrastructure, including multinational, multi-business and merging organizations, identity infrastructure is often stitched together without broader visibility.

Spotlight Cross Domain & Trust Visibility provides insight to understand where AD domains across companies have too much bi-directional trust, which can result in identity risk and lateral movement by attackers. Business leaders can gain a centralized view into the broadest organizational structure’s domains and trusts to better prevent identity risk exposure in a holistic fashion.

Sigma Information Protection Platform

Since its introduction in early 2020, Proofpoint’s information protection business has grown a remarkable 107%, making the company the second largest data loss prevention (DLP) vendor globally and by revenue according to Gartner. Driven by the accelerated adoption of work-from-anywhere practices, the Proofpoint Sigma Information Protection platform is now deployed to over 5,000 customers and 46 million users worldwide, analyzing 45 billion events each month, and trusted by nearly half of the Fortune 100.

Proofpoint’s Information Protection platform merges content inspection, threat telemetry and user behavior across channels in a unified, cloud-native interface.

Privacy by Design Data Loss Prevention

As international organizations work to meet new and changing local privacy and data sovereignty requirements, Proofpoint now hosts its Sigma Information Protection platform in regions such as the European Union, Japan, and Australia in addition to the U.S.

Proofpoint is also further investing in privacy-related capabilities so that organizations can mask sensitive data in the console to limit its exposure and create custom data access policies to address privacy and compliance needs. 

Additional features are available in beta, with general availability expected in Q3 2023, enabling organizations to anonymize identifying user information so analysts can investigate without bias and with better privacy for the user.

Administrators will also be able to set up metadata for anonymization and approval workflows for de-anonymizing the metadata during investigation.

The post Proofpoint Unveils New Innovations to Combat Increasingly Common Threats appeared first on My TechDecisions.

Early AI adopters are often tech enthusiasts, eager to leverage its competitive benefits. However, in their eagerness to embrace AI, they may overlook critical steps that are fundamental to AI adoption. At the other end of the spectrum, established organizations with deeply entrenched processes may be reluctant to make the necessary changes to reap AI’s benefits.

Let’s explore the obstacles to business AI adoption, the reasons AI solutions often fall short of expectations, and some solutions for successful AI adoption.

Common Points of Failure

New technologies often come with a steep learning curve. AI/ML engineers must acquire extensive knowledge about potential use cases in real-world scenarios, and translate abstract concepts from stakeholders into usable models that can be practically deployed.

At the same time, adopters must be sold on the value of AI technology, and the feasibility of its deployment. They need to factor in costs, onboard specialized talent, create a plan for integrating AI with established systems, and garner buy-in from stakeholders.

Potential points of failure for AI adoption include:

• Unclear definition of problem(s) you want AI to solve.
• Lack of sufficient amounts of high-quality data to train and implement your ML model after you have spent a lot of time on research.
• Failure to sell the concept of AI to stakeholders.
• Inability to build and maintain a robust ML infrastructure.
• Problem of acquiring and building the right talent that is specialized enough to fuel AI transformation.
• Failure to educate your workforce on the value of AI and how it will impact their workflow.
• Inaccurate assessment of total costs associated with AI adoption, including costs for IT infrastructure, managing ML models in production, employee training, and costs associated with systems integration.

Careful planning can help streamline the adoption process, mitigate roadblocks along the way, and achieve high ROI on AI investment.

Why AI Solutions Sometimes Underperform

Business leaders who drive marketplace innovation often embrace AI with open arms. However, adopting new technologies comes with certain risks. AI is still a novel concept, with a short history of real-world trial-and-error, and investing in AI is a leap of faith.

Many organizations buy into the promises of AI transformation, only to find that their solution underperforms. The following issues are often to blame:

Misconceptions about what AI can and cannot do

Artificial intelligence has enormous potential to do away with mundane tasks that undermine workforce morale and eat away at profits. AI can eliminate human error, streamline multiple processes, and reap critical insights from data that impact your bottom line. AI is not human. It cannot create, strategize or set goals for you. Be aware of that.

Lack of understanding between business stakeholders and developers

AI/ML engineers need concrete instructions and succinct information to write code and train algorithms. However, business executives often speak in generalities. They do not speak tech or think in technical terms. Developers and stakeholders need to find common ground if the AI project is to succeed.

Unclear objectives

Prior to considering AI adoption, organizations need to ask critical questions about how AI can enhance their business operations:

• How do we measure AI performance, and what KPIs signal success?
• What problem do we need to solve? Are we solving the right problem?
• Is AI the best solution? Can simple business rules be a better solution?
• What does successful AI deployment look like?
• What organizational and technological changes will we need to make to implement AI?

Insufficient quality and quantity of data

AI relies on sufficient amounts of quality data to build algorithms and train models. If collecting and managing data is not your organization’s strong suit, you may not be ready for AI adoption.

Before launching your AI project, take stock of your data, where it is stored, whether it comes from in-house or a third party, and who can access it. Data quality is vital to building and training accurate ML models. To make data useable for your project, a data engineer will need to cleanse, convert and manipulate it.

Developing models in a conceptual bubble

It is not enough to build and test ML models in a controlled environment with curated data. AI solutions need to function in real-world scenarios, with imperfect data, in the face of real-world problems. Consider the environment in which AI will be deployed, and test it in a realistic setting. In addition, ML models must be continuously monitored in production, to account for data and model drifts.

Lack of long-term planning

Model creation is not an end in itself. Data is continually changing, and ML models must be continually maintained, retrained and updated. This requires an ongoing budget for qualified personnel, computing power, and policy updates as your system evolves and scales.

Getting the Most from Your AI Investment

Every business has unique needs, and there is no one-size-fits-all AI solution that you can simply plug into your existing systems and expect it to perform. The good news is that you can take concrete steps to ensure that your AI project gives you a satisfactory return on investment.

1. Lay a solid foundation for AI adoption by defining what problem(s) you hope to solve.
2. Work with an AI expert to map out your AI journey.
3. Set short-term and long-term goals. AI is still evolving, and data is ever-changing, so be prepared for future changes.
4. Consider how AI will change your business processes and operations, how you will integrate or phase out old processes, and how it will impact your workforce.
5. Budget for the ongoing development of your IT and ML infrastructure. This is necessary to help your AI initiatives scale organization-wide.
6. Train your workforce to use the new technology, and prepare them for coming changes. The better they understand AI and know what to expect, the more likely they are to get on board with your AI transformation.
7. Be considerate of key principles of ethical AI.

Now is the Time to Begin Your AI Journey

Artificial intelligence is here to stay, and early adopters are sure to gain a competitive advantage. As AI technology expands, the global business landscape will be forever transformed. But the transformation is still in its early stages.

Now is the perfect time to leap into business AI adoption. Armed with the knowledge provided here, you can avoid common pitfalls and set yourself up for success by strategically mapping out your AI journey.

Aleksandr Chaptykov is the senior machine learning engineer at Provectus. His contributions have played a vital role in the success of many digital products in Provectus. His areas of interest include NLP, recommendation systems, RL. He is the author of multiple publications on AI/ML and an IT conference speaker.

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