Artificial Intelligence Archives — ľAV /category/artificial-intelligence/ Powerful solutions for a complex world Mon, 06 May 2024 22:12:38 +0000 en-US hourly 1 https://wordpress.org/?v=6.5.3 /wp-content/uploads/2019/03/Red-Mark-600px@2x-100x100.png Artificial Intelligence Archives — ľAV /category/artificial-intelligence/ 32 32 What Should Your AI Factory Look Like? /ecs-insight/article/what-should-your-ai-factory-look-like/ Mon, 06 May 2024 21:00:45 +0000 http://ecstech.flywheelstaging.com/?p=29331 Learn how the “AI factory” approach to AI development accelerates time-to-value and helps unlock the full potential of AI for your operations.

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By Martin Klein
Vice President of Analytics and Artificial Intelligence

Accelerating Time-to-Value With Automated AI Development

It’s never been more important to leverage the computational power of AI to secure lasting strategic and tactical advantages, from the battlefield to the boardroom. However, if your AI development pipeline isn’t optimized for scalability and efficiency, it’s impossible to realize the full potential of enterprise AI.

Enter the “AI factory” approach to AI development, a systematic and streamlined methodology for building, deploying, and maintaining AI models at scale for the enterprise. Let’s explore:

  • How AI factories represent a significant evolution over traditional methods of AI development.
  • The potential challenges to implementation.
  • Why leaning on a proven partner may be the most cost-effective route to realizing these transformational capabilities for your organization.

AI Factories: Systematic, Automated AI Production

First, let’s define the AI factory approach to AI development. In many ways, AI factories are like traditional manufacturing assembly lines. While different in terms of what they produce and how they operate, they share the underlying principle of systematic production. The goal for an AI factory is to leverage automation to decrease, to the extent possible, unnecessary human intervention.

AI factories integrate various components of the AI lifecycle — such as acquiring data, preparing data, model training, testing, deployment, and monitoring — into a cohesive and automated workflow. They do this through a combination of advanced tools, standardized processes, and infrastructure designed to streamline the creation, training, and deployment of AI models.

On the modern battlefield, the edge goes to those who can predict, automate, and optimize.

Learn how algorithmic warfare plays a key role in employing our nation's integrated deterrence strategy.

Too often, end users lack insight into how “black box” AI models generate actionable information.

Learn how XAI puts the end user back in the driver’s seat.

How Exactly Do AI Factories Work?

Here’s a seven-step overview of how the automated development workflow of an AI factory typically works:

1. Data Management

Automation begins with data collection, preprocessing, and management. AI factories use tools to automate data cleaning, transformation, labeling, and augmentation, ensuring that datasets are ready for training with minimal human intervention. Data pipelines are also set up to continuously ingest and process new data, keeping models up to date.

2. Model Development

In an AI factory, model development is standardized and automated using machine learning (ML) frameworks and libraries. These can be customized for specific tasks but provide a starting point that automates much of the boilerplate code required for model training.

3. Hyperparameter Tuning and Optimization

AI factories often incorporate automated machine learning (AutoML) tools to automate the selection of algorithms and the tuning of hyperparameters. These tools use search algorithms to explore different model configurations, automatically selecting the best performing models based on predefined criteria.

4. Testing and Validation

Automated testing frameworks are used to evaluate model performance against benchmarks and validation datasets. These frameworks can automatically run a series of tests to ensure that models meet specific accuracy, reliability, and performance standards before they are deployed.

5. Deployment and Integration

Once a model is ready, AI factories automate the deployment process, including integration with existing systems and applications. Continuous integration and continuous deployment (CI/CD) pipelines are used to automate the testing, containerization, and deployment of models, ensuring that they can be quickly updated and scaled as needed.

6. Monitoring and Maintenance

After deployment, AI factories use automated monitoring tools to track model performance, resource usage, and operational metrics. Anomalies, model drift, or decreases in performance trigger automated alerts and may suggest the implementation of champion/challenger techniques to optimize, adjust, and retrain models for long-term effectiveness. It is critical that AI deployed in government applications include evidence packages that ensure outputs are trusted and explainable.

7. Feedback Loops

Automation in AI factories also includes setting up feedback loops that allow the system to learn from its performance and the decisions it makes. This feedback is used to reinforce models and adjust workflows, creating a cycle of continuous improvement.

Faster Returns, Rapid Scaling, and Enhanced AI Solutions

Key benefits of AI factories include:

Accelerated Time-to-Value.

The AI factory approach minimizes bottlenecks in AI development, ensuring your organization sees faster returns on AI investments. Relying on automated pipelines will allow you reduce the time and resources required to bring AI models into production. This streamlining enables rapid scaling, allowing you to respond quickly to changing business needs and operational demands.

Improved Consistency and Quality.

Automation and standardization within the AI factory promote consistency in model development and deployment. This, in turn, enhances the quality of AI solutions, reducing the risk of errors and improving overall reliability.

Resource Optimization.

As you optimize costs associated with AI development, your organization can better allocate compute and storage resources, ensuring that skilled data scientists can focus on high-value tasks while routine processes are handled seamlessly by the AI factory.

New Tools, Upskilling, and Other Implementation Challenges

Implementing the AI factory approach is not without its challenges. While automation is a key strength of AI factories, it’s not an excuse to jettison human oversight altogether. It’s essential to strike a balance, ensuring that human experts are involved in critical decision making and validation.

It’s also critical to have robust cybersecurity measures in place to safeguard the high volume of data moving through your AI factory. Finally, it’s worth noting that implementation usually requires teams to adapt to new tools and methodologies. You may face staffing challenges and increased costs while upskilling your workforce.

MARTIN KLEIN
Vice President of Analytics and Artificial Intelligence

ľAV: A Proven Partner in AI Development and Deployment

Do you have the expertise to judge which tools best serve rapid AI development? Do you have clarity around how expensive it could be to adequately upskill your teams? Are you following cybersecurity best practices to protect your tools from hacking and misuse?

If you’re unsure of your response to any of these questions, ľAV wants to hear from you. We would love the opportunity to continue the conversation, learn about the unique challenges facing your organization, and share more insights from our approach to AI development.

Now that you know what your enterprise AI factory should look like, are you ready to realize the full potential of AI for your operations?

Reach Out To Our Experts

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]]> The Power of Predictive Security: How AI Helps Prevent Cyberattacks /ecs-insight/blog/the-power-of-predictive-security-how-ai-helps-prevent-cyberattacks/ Tue, 16 Apr 2024 11:00:23 +0000 http://ecstech.flywheelstaging.com/?p=26063 ľAV Pathfinder leverages AI to deliver predictive security, enabling organizations to identify and mitigate risks before bad actors attack.

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By
Director of Cyber Analytic Products

In a hyper-connected world where cyber threats are evolving at an alarming pace, traditional reactive security measures are no longer sufficient to safeguard sensitive information and critical infrastructure. As a result, cybersecurity professionals’ focus has shifted towards proactive and predictive security solutions that can anticipate and thwart cyberattacks before they strike.

The technology that makes this possible? Artificial intelligence (AI), an increasingly critical component of modern cybersecurity.

The Need for PredictiveCybersecurity: Shifting the Paradigm

Traditional cybersecurity strategies are akin to locking the doors of a house after an intruder has already gained access. Reactive approaches entail identifying and responding to threats as they occur, leaving little room for proactive defense.

Predictive security is more like knowing a would-be intruder is coming before they ever make it to your front door. It represents a conscious paradigm shift, enabling organizations to anticipate and mitigate potential risks before bad actors attack. By harnessing the power of AI, your cyber analysts are better equipped to analyze vast amounts of historical data, identify patterns, and predict attack vectors with unprecedented accuracy.

A More Intelligent Arsenal

AI algorithms can continuously learn from real-time data, enabling your cybersecurity tools to adapt to evolving threats and making your whole security posture more nimble and agile. By analyzing historical cyberattack patterns and discerning the tactics employed by malicious actors, your analysts can predict and prioritize potential vulnerabilities that may be exploited.

Enter ľAV Pathfinder

Identifying your organization’s need for predictive security is an essential first step, but how do you meet that need? Where do you turn for the expertise to execute this paradigm shift in your cybersecurity?

Enter ľAV Pathfinder, an AI-powered cyber analytics platform that leverages historical data and advanced algorithms to predict exploitable entry points and help counter cyberattacks.

Pathfinder’s predictive capabilities are based on three fundamental pillars:

ATTACK PROBABILITY

Pathfinder’s AI scrutinizes historical attack data and determines the likelihood of certain cyber threats occurring. By quantifying the probability of various attacks, organizations can focus their resources on the most pressing threats and determine which can be scheduled for triage at a later date.

PERVASIVENESS

Not all vulnerabilities are equal; some may be widespread and impact numerous systems, while others are niche and affect only specific configurations. Pathfinder considers how pervasive potential vulnerabilities are, ensuring that high-risk, widespread vulnerabilities are addressed immediately.

URGENCY

Pathfinder identifies the criticality of vulnerabilities and determines the potential harm they may cause if exploited. Prioritizing urgent vulnerabilities allows organizations to allocate resources efficiently and protect against the most severe threats.

Force Your Adversaries Into a Losing Battle

The real strength of predictive security lies in its ability to force adversaries into an unfavorable position. By proactively addressing potential weaknesses, organizations employing ľAV Pathfinder can create a lower-risk environment, making it increasingly difficult for attackers to find exploitable entry points. As the organization’s defenses become more robust and resilient, malicious actors encounter diminishing returns for their efforts — a powerful deterrent.

The power of predictive security, fueled by AI algorithms and historical data analysis, is revolutionizing the way organizations defend against cyberattacks. ľAV Pathfinder can force your adversaries into an uphill battle, meaning your organization can stay one step ahead in an ever-evolving threat landscape.

Ready to shift the paradigm?

Contact Our Experts

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]]> Work That Matters: Technical Architect Stephen Giuliani Wants to Unleash Collaboration in Government Cloud /ecs-insight/ecs-culture/work-that-matters-technical-architect-stephen-giuliani-wants-to-unleash-collaboration-in-government-cloud/ Wed, 03 Apr 2024 04:01:30 +0000 http://ecstech.flywheelstaging.com/?p=29031 Stephen Giuliani, a lead technical architect at ľAV, discusses what it means to bring together the best teams to solve complex problems and the importance of being proactive and pushing the envelope.

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Stephen Giuliani, a lead technical architect at ľAV, has a history of pushing technological innovation and collaboration to help solve massive challenges.

Stephen’s career has been defined by the need to work well with disparate teams. That was true when he worked on Project Salus, the Department of Defense (DoD) AI project that played a critical role in providing predictive analytics on the effects of COVID-19 (including the impact on DoD command logistics and future planning). And it’s been true as he’s continued to manage multi-hybrid-cloud enclaves for the DoD. His goal? Helping the government respond more effectively to the many national security threats our nation faces, as well as the most critical needs of its citizens.

We sat down with Stephen to discuss what it means to bring together the best industry teams to solve complex problems. Along the way, we got insight into how ľAV stands apart as a systems integrator executing great ideas.

Q: Could you give us an overview of the work you do?

A: Technical architects help plan, design, and build IT systems. In my case, for several years I’ve been focused on cloud architecture and infrastructure, which, when you’re working primarily with the government and specifically the DoD, means building operational environments that are secure, adaptive, and agile. It also means understanding the balance of the role existing systems play versus the benefits of innovation, migration, etc.

A few years back, I was involved with Project Salus, which leveraged predictive analytics to predict everything from COVID-19 hotspots to logistical issues, to which military bases were best for stockpiling food and supplies. Salus also provided statistical insights into COVID-related anomalies and emergent phenomena that could affect DoD’s interests. My part in that was not only acting as the primary data engineer, but also helping design and build the underlying infrastructure that our mission partners — Amazon, IBM, Microsoft, Dell, Humetrix, LUCD, and others — needed to run their operations.

So, the throughline or connective tissue here is collaboration. I help build environments that enable mission partner collaboration on a critical scale, without sacrificing security.

Q: You talk about enabling greater collaboration at the DoD and across the government. Why is that such a critical issue?

A: So, the very nature of these agencies and the work they do has an impact, whether subtle or overt, on the day-to-day life of every citizen of this country. They’re doing critical mission work, and one thing that can happen is solution providers can get stuck in a reactionary mindset where they wait for the customer, be it the DoD or whomever, to bring problems to them before acting. That leads to suboptimal, less efficient government responses. And we most often see this in environments where there’s just one provider trying to do it all themselves, with less collaboration and less drive towards innovating new solutions.

The alternative is enabling greater collaboration within environments that are still secure. Collaboration fosters innovation, integration, adoption, and vision beyond what I call the “hive mindset.”

Q: What is it about ľAV’ approach to delivering cloud infrastructure that enables collaboration and, by extension, innovation?

ľAV sees the value not only in bringing a proactive mindset to solution delivery, but also in pushing the envelope and even challenging our customers a bit at times. If ľAV has a vision for how the DoD can leverage more collaborative network infrastructure to achieve better outcomes faster, it’s incumbent on us to articulate and demonstrate that.

Can we bring mission partners in at a secret level to do constructive work? Can we set up network infrastructure that meets the same infrastructure requirements that you would hold U.S. citizens to, while allowing other communities to access it in a controlled, secure way?

We think so, but there’s an important balance to strike here because we can’t ignore our customers’ very real and valid concerns around data security. Security is of paramount importance. So, it’s in that balance where you’re ultimately going to innovate. And we get there by bringing in and integrating the best teams and the best ideas, because we know that we’re not the only ones with great ideas, but we have a proven record of executing them.

Q: Any final insights you’d like to leave us with?

A colleague of mine once used the phrase “the art of the possible” to describe how we should think about technical transformation and collaboration in these environments. It’s worth asking your customer to rethink how they do things versus what’s possible. Are you doing it this way just because that’s how you’ve always done it? Are there opportunities to do something differently, more efficiently, more beneficially than before? So, I think presenting that question, but in very pragmatic terms, is essential.

And finally, I’ll just say in cases when we work on something truly innovative — and by innovative, I mean the thing didn’t exist before and now it does or could — that’s hugely rewarding. Sure, it’s disappointing if it’s not ultimately adopted, but even those times provide learning opportunities and fuel for further innovation. You know that in this line of work you’ll ultimately get to see the fruits of that labor.

“Work That Matters” is a series in which ľAV experts discuss their roles and responsibilities and the larger impact they have in the workplace, community, and world.

Careers at ľAV

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]]> Building the Data Driven Future: Data Inventory, Data Mesh, and the Federal Data Strategy /ecs-insight/article/building-the-data-driven-future-data-inventory-data-mesh-and-the-federal-data-strategy/ Tue, 19 Mar 2024 09:00:42 +0000 http://ecstech.flywheelstaging.com/?p=28735 To meet the standards of the Federal Data Strategy and begin leveraging data to its full strategic potential, federal organizations should implement data mesh architecture, founded on a robust data inventory.

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By
Director, ľAV Data & AI CoE

Senior Data Solutions Architect

Data Solution Architect

Portfolio Director

Data Mesh and Data Inventory

First, a review of our core concepts of data mesh and data inventory:

Data mesh represents the next step in the evolution of data architecture. It’s a maturation from data lakes — which tend to deteriorate into unusable “data swamps” over time — to a more flexible, comprehensive solution that focuses on data products and strategic data assets. It also helps organizations develop practices to secure, manage, share, and create a common language for data within their enterprise while preparing to implement new AI-enabled tools.

The Data Mesh Pyramid

Data governance and intentional maturity provide a backbone for improved visibility into data use and ultimately data products.

The data inventory is a high-level index of the data in the enterprise that is both machine and human readable. Like an index at the end of a textbook, it documents an essential set of facts about the data and acts as a foundational resource for referencing and finding data in the enterprise.

In a mature data mesh, the data inventory acts as an index of raw material that can be developed into data products, which are published in a robust data catalog. Users can access those data products to support mature data services and analytics, positioning agencies to make use of emerging and anticipated technologies like AI/ML, , and .

It’s worth taking a moment to make a clear distinction between the data inventory and data catalog, as it may not be immediately clear when a data consumer would want to seek out data from one versus the other. The key difference lies in the nature of the data being sought and who is seeking it. Data catalogs contain ready-to-use data products. For the vast majority of your enterprise’s consumers, the catalog is where they will go to retrieve the relevant data products they need to drive their services and/or analytics. The inventory, on the other hand, simply confirms that the data exists somewhere in the organization, even if there’s no current way to make use of it. Hence, it’s your enterprise’s domain experts who will use the data from the inventory to curate new data products for the benefit of the enterprise.

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Supporting Government Transparency

Meeting the Federal Data Strategy With ľAV

For federal agencies, the transformation into AI-ready, machine-readable, and machine-reasoning organizations is at hand. ľAV’ experts are committed to helping your organization evolve toward the enterprise model of the future.

Contact Our Experts
REFERENCE: Dehghani, Zhamak. Data Mesh: Delivering Data-Driven Value at Scale. O’Reilly Media, 2022.

GLOSSARY

Data Mesh — A decentralized data architecture that organizes data by a specific business domain, provides more ownership to the producers of a given dataset, and allows them to set data governance policies, enabling self-service use across an organization.

Data Inventory — An index of all the data within an organization and the foundation for data mesh architecture.

Data Product — A self-contained data “container” that directly solves a business problem or is otherwise monetized; e.g., an application or tool that uses data to help businesses improve their decisions and processes.

Data Catalog — Uses metadata — data that describes or summarizes data — to create an informative and searchable inventory of all data products within an organization.

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]]> ľAV Awarded $190M AI/ML Contract from DEVCOM Army Research Lab /ecs-insight/press-release/ecs-awarded-190m-aiml-contract-from-devcom-army-research-lab/ Tue, 05 Mar 2024 12:00:01 +0000 http://ecstech.flywheelstaging.com/?p=28500 ľAV will help develop advanced capabilities that provide new AI mobility and AI agility across multiple critical DoD programs and domains.

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Company expands its AI/ML support for the Department of Defense

FAIRFAX, Va. ― March 5, 2024 — ľAV, a leader in advanced technology, science, and engineering solutions and an ASGN (NYSE: ASGN) brand, received a three-year, $190 million contract from the United States Army Combat Capabilities Development Command (DEVCOM) Army Research Lab (ARL). As prime contractor, ľAV will serve as a system integrator for DEVCOM ARL, delivering and integrating artificial intelligence (AI) capabilities for Department of Defense (DoD) services and combatant commands.

The contract, awarded in Q3 of 2023, is a new initiative helping aid the DoD’s objectives through the expansion and extension of key AI research. ľAV will help develop, experiment with, and demonstrate advanced capabilities that provide new AI mobility and AI agility, at theater/field-level, across multiple critical DoD programs and domains.

MARSHALL THAMES

Senior Vice President, Mission Solutions

“ľAV has a long history of providing mission solutions to the DoD including cloud, cyber, data, and AI solutions,” said Marshall Thames, senior vice president of mission solutions at ľAV. “We’re honored to support such a critical mission: developing and deploying next-gen AI capabilities along with our network of empowered teammates representing the best AI talent in the world.”

“As the DoD looks to increase its AI capabilities, they face a technically challenging problem set,” said John Heneghan, president of ľAV. “As one of the largest providers of AI solutions to the DoD, ľAV will lean on our broad AI expertise to help the DoD meet its automation objectives: increasing the safety, readiness, and decision dominance of our nation’s warfighter.”

JOHN HENEGHAN

ľAV President

About ľAV
ľAV, ASGN’s federal government segment, delivers advanced solutions in cybersecurity, data and artificial intelligence (AI), cloud, application and IT modernization, science, and engineering. The company solves critical, complex challenges for customers across the U.S. public sector, defense, intelligence, and commercial industries. ľAV maintains partnerships with leading cloud, cybersecurity, and AI/ML providers and holds specialized certifications in their technologies. Headquartered in Fairfax, Virginia, ľAV has more than 3,900 employees throughout the United States. For more information, visit ľAVtech.com.

About ASGN
ASGN Incorporated (NYSE: ASGN) is a leading provider of IT services and solutions across the commercial and government sectors. ASGN helps corporate enterprises and government organizations develop, implement, and operate critical IT and business solutions through its integrated offerings. For more information, please visit .

Contacts
Shab Nassirpour
Vice President, Marketing and Communications, ľAV
(703) 270-1540
Shab.Nassirpour@ľAVtech.com

Kimberly Esterkin
Vice President, Investor Relations, ASGN
Kimberly.Esterkin@ASGN.com

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]]> Train for Tomorrow: Build a Career in Cybersecurity and AI /ecs-insight/article/train-for-tomorrow-build-a-career-in-cybersecurity-and-ai/ Thu, 22 Feb 2024 12:00:35 +0000 http://ecstech.flywheelstaging.com/?p=28284 Check out how ľAV training and career development tools are helping employees evolve their careers and achieve long-term career goals.

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In the fast-evolving world of cybersecurity and AI, how do you keep your skills on the cutting edge? At ľAV, we provide our employees with resources to succeed in their careers now and into the future.

SEE WHAT ECERS HAVE TO SAY ABOUT CAREER DEVELOPMENT

“

At ľAV, we are constantly looking toward the future of AI. My job is to give people the resources to develop their careers as they explore and shape the future of the industry.

Patrick ElderDirector, Data and AI CoE
“

The ever-evolving nature of cybersecurity poses a challenge for professionals hoping to stay on the forefront of the industry. At ľAV, we take a proactive approach to training and career development, preparing our employees not just for today’s challenges, but tomorrow’s as well.

Brent DuckworthVice President, Cyber CoE
“

Since July of last year, I have earned nine different Microsoft certifications and I have used ľAV University to prepare for almost all of them. ľAV internal training tools are an integral part of my exam preparation regimen.

Adam MiceliManaging Consultant, Enterprise Solutions
“

I recently took a course for ISC2 - CC Certified in Cybersecurity, and ľAV training programs helped me pass the exam in one attempt! In the future, I plan to leverage ľAV University’s extensive library of content to help me satisfy my CPE and PDU requirements for my CC and PMP certifications.

Madison PotvinProject Management Officer, Information Security
“

I participated in the Elasticsearch Engineer training cohort in June 2023 that was facilitated by ľAV's training program. I found the training to be top-notch, covering both concepts and practical application of working with the Elasticsearch search and analytics engine.

Gregory ScheidelChief Cybersecurity Officer
“

ľAV affords me the opportunity and financial support to engage in training that will enhance my career development. My management team consistently backs my requests for external training and readily offers guidance on the most advantageous paths to follow.

Steven CremersSOC Manager, MSP
“

Through ľAV University, I used Pluralsight's training courses to study for and pass the CompTIA Security+ test. Next, I am planning to take the Automating Networks course available on Pluralsight!

Ali FarhaniSenior Network Engineer, Mission Solutions
“

I have found a lot of benefits using ľAV internal training tools. Recently, I used the AWS tools on Pluralsight as a resource to study for and pass the AWS Architecture Associates Exam.

Barialai FarahiSystems Engineer, Mission Solutions

Train for Tomorrow’s Challenges in Cybersecurity and AI

Learn more about ľAV’ approach to training and career development.

ľAV University

Our central repository for all things training and development. ľAV employees can access ľAV University to learn how to  gain new skills, achieve certifications, and advance their careers in everything from cybersecurity and AI to project management and IT operations.

Pluralsight

ľAV is proud to partner with Pluralsight, a skill development platform that offers classes taught by industry experts and personalized to your skill level. Topics include software development, data and AI, infrastructure, cloud, security, DevOps, project management, and more!

Communities of Excellence (CoEs)

At ľAV, our CoEs support the growth of employees striving to stay on the cutting edge of technology. They develop best practices to help serve customers at the highest level, while also providing mentorship and guidance to employees regarding certification paths, project opportunities, and placement.

Cohorts and Working Groups

At ľAV, we employ peer-to-peer learning so employees don’t have to take on career development alone. Join a cohort of colleagues with similar skill levels and work together to earn new certifications and accreditations.

$5,000 Training Benefit

With $5,000 in education benefits per year, you can explore the techniques and skills to take your career to the next level. Think there is a skill worth learning? Our training team works with you to find the best tools and accreditation to achieve your goals.

Student Loan Support

To ease the transition from school to the workplace, we offer tuition reimbursement for new employees — up to $3,000 per year for up to four years of an advanced STEM degree.

Premier Technology Partnerships

ľAV is a premier technology partner of industry giants like Amazon, Google, Microsoft, ServiceNow, and Elastic. Here, you will work on cutting-edge solutions used across the industry while learning from the field’s biggest experts.

Training Director Casey Van Camp on IT Career Development

Choosing a Forward-thinking Company

Preparing Yourself for Tomorrow’s IT Landscape

When Training Benefits are Not Enough

JOIN US. EVOLVE YOUR IT CAREER.

See Job Openings

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]]> Decoding The Battlefield: The Power of Algorithmic Warfare /ecs-insight/article/decoding-the-battlefield-the-power-of-algorithmic-warfare/ Wed, 14 Feb 2024 12:00:03 +0000 http://ecstech.flywheelstaging.com/?p=28092 ľAV data and AI experts are ready to help our warfighters leverage AI on the frontlines to optimize decision making and secure lasting strategic and tactical advantages.

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By Vice President of Analytics and Artificial Intelligence

Leveraging AI on the frontlinesis transforming military strategy, optimizing decision making, and revolutionizing warfare.

On the modern battlefield, the decisive edge goes to the nation with decision dominance — the ability to leverage data and decision analytics to predict adversary actions, automate responses, and optimize operations. The result: leaders can visualize the battlespace at machine speed.

Algorithmic warfare, the integration of advanced algorithms into military operations to enhance decision making (across the warfighting functions of intelligence, maneuver, fires, logistics, and C2 systems), represents the cutting edge of this data-driven approach.

Algorithmic warfare will play a key role in employing our nation’s integrated deterrence strategy. Leveraging the power of cross-domain data, surfacing insights at machine speed, and allowing battlefield commanders to see first and act decisively will be key to both preventing war and winning decisively on the modern battlefield.

Algorithmic warfare is about leveraging the computational power of AI to secure lasting strategic and tactical advantages on the battlefield. Understanding the benefits, challenges, risks, and future developments of this frontier technology is critical to maintaining decision dominance for leaders operating from the boardroom to the battlefield.

Augmented DecisionMaking at Speed and Scale

Integrating AI-enabled decision support into operations yields several key benefits, including:

1

Augmented Decision Making. AI algorithms will never replace the skill and intuition of a battlefield commander. While AI can assist commanders in how they visualize the battlespace, there’s no substitute for decades of experience garnered in the study of political, military, economic, and informational dynamics. To suggest otherwise would question the foundation of responsible AI. What AI can do, however, is process and analyze data at speeds and scales unattainable by humans. This provides decision makers with actionable insights and allows personnel to focus on tasks such as the evaluation of risk, the ability to integrate outcomes, and future options on the battlefield.

2

Integrated Warfighting Systems. Algorithmic warfare enables greater integration of warfighting systems on the modern battlefield. Integrating what have, traditionally, been disparate battlefield systems (e.g., logistics, intelligence, maneuver, and fires) optimizes these functions across the board.

3

Automated Response Systems. AI can enable quicker responses to threats, potentially intercepting hostile actions before they escalate. Automating data processing and exploitation can also streamline workflows for analysts, allowing them to focus on producing high-quality intelligence products.

4

Resource Optimization. Algorithmic warfare can streamline supply chains and resource allocation, ensuring optimal use of assets.

Challenges to Implementation

It’s critical to note that AI is no panacea, and implementing AI-enabled decision support is not without its challenges. Any successful attempt at algorithmic warfare will have to contend with these, and other, problems:

1. Reliability and Security. AI systems must be incredibly reliable and secure to be trusted in high-stakes mission environments. The emerging intersection of cybersecurity and AI will be crucial going forward, as system vulnerabilities can have catastrophic consequences.

a. Relatedly, because the United States’ integrated deterrence strategy is founded on coalition-building and leveraging our global partners, another major challenge is integrating these capabilities across mission partners. Those partners can be as varied as the Department of State, the U.S. Agency for International Development (USAID), foreign nations, and, in some instances, commercial entities.

2. Data Quality and Model Bias. AI algorithms require high-quality data for training. Poor or biased data can lead to inaccurate or unethical outcomes.

3. Technological Integration. Integrating AI into existing infrastructures poses significant technical challenges and can require either a substantial investment or a partner with proven expertise.

4. Ethical and Legal Concerns. The use of AI in warfare raises significant ethical questions, particularly regarding autonomous weapons systems. Legal frameworks governing AI use are beginning to take shape but are still in nascent stages in many respects.

What Does the Future Hold?

Prophesying about AI often turns out to be a fool’s errand, as this technology continues to evolve at an exponential rate and in ways that are often surprising. Change is the only constant, and the Department of Defense (DoD) must acquire capabilities that avoid vendor lock-in and future-proof against continued technological advances.

Still, there are some developments on the horizon that we can feel reasonably confident about, and they will bear heavily on the future of algorithmic warfare.

Increasing System Autonomy

AI decision support systems will continue to evolve, and while human oversight will remain necessary, these systems will only get better at making rapid, context-aware decisions that carry significant mission impact.

Greater Human-AI and Machine Teaming

The fears that AI is poised to replace human decision making are fundamentally misplaced; however, AI will continue to augment human decision making with greater effectiveness. Interconnected defense ecosystems will also provide AI decision support systems an opportunity to seamlessly collaborate and share threat intelligence, improving security and reliability.

Improved Explainability

To address concerns over the opacity of AI algorithms, particularly in legally fraught settings such as conflict areas, future AI development will include a focus on enhancing explainability. This will be critical in building trust and understanding the decision-making processes of AI systems.

More Ethical AI

The focus on developing ethically-aligned AI systems will intensify, with robust legal and regulatory frameworks taking shape to govern their use.

“We are witnessing the ways wars will be fought, and won, for years to come.”
— Ret. Gen. Mark Milley, former Chairman of the U.S. Joint Chiefs of Staff

AI Insights at the Speed of the Mission

Algorithmic warfare represents a revolutionary approach in military strategy, one driven by data-fueled insights that can decode the battlefield in near real-time. Our warfighters are leveraging AI from the boardroom to the battlefield to optimize decision making and secure lasting strategic and tactical advantages. ľAV’ data and AI experts stand ready to help derive actionable insights from vast amounts of data, develop and deploy AI, and manage the unmanageable.

Without optimized data architecture, AI is doomed to underdeliver. Learn how distributed data architecture — data mesh — helps organizations leverage data as a strategic asset while meeting the standards of the Federal Data Strategy.

MARTIN KLEIN
Vice President, Analytics and Artificial Intelligence

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]]> Escaping the Data Swamp /ecs-insight/article/escaping-the-data-swamp/ Wed, 24 Jan 2024 12:00:45 +0000 http://ecstech.flywheelstaging.com/?p=27859 Data lakes have long been the standard for data storage, but they tend to devolve into disorganized “data swamps” that hinder productivity and threaten compliance. The solution? Maturing your data architecture with data mesh.

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Using Data Mesh to Create a More Efficient and Sustainable Data Ecosystem

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By ,
Director, Data & AI CoE
,
Director, Data & AI
,
Vice President, Cloud Operations

From Warehouses to Lakes to Swamps: How Did Data Architecture Get Here?

Decades ago, when maturing organizations began to recognize a need for systems that could manage and analyze large volumes of data, the first data warehouses were born. This enabled centralized, standardized reporting and analysis, but over time it became clear that making changes in data warehouses was too slow to keep up with the mission’s pace. What followed? Data lakes, unstructured repositories that data flows into without transformation, enabling centralized teams to take on the data preparation load for rapidly evolving analytic needs.

Unfortunately, data lakes tend towards disorganization over time, which is where the term “data swamp” came from. Much like a swamp’s murky waters, the ungoverned mixing of data sources and types makes it increasingly difficult for analysts to navigate, especially at mission speed.

The need for clarity, efficiency, and sustainability brings us to the next step in the evolution of your data architecture: a distributed model, a.k.a. data mesh.

Data mesh was embraced by the U.S. Army in the October 2022 Army Data Plan

Escaping the Data Swamp
WithData Mesh

 

Data mesh architecture’s distributed model of data management represents a leap forward in organizational maturity. By decentralizing data ownership to domain experts focused on the creation and maintenance of data products, then making those products discoverable within a centralized, curated data catalog, data mesh transforms the way your organization handles and utilizes data.

Here are the three essential ways that data mesh improves upon traditional
 data architecture and helps your organization escape the data swamp:

1. Decentralized Ownership and Federated Governance

What It Is: Centralized data architecture creates bottlenecks, scalability challenges, and a lack of agility. In contrast, data mesh embraces decentralization, which fosters a more dynamic and scalable approach to data management, and federated governance, which enables seamless integration and collaboration across different parts of an organization.

The Advantages: One of the key advantages of decentralized ownership lies in creating a direct line of communication between data users and data owners/producers, allowing the mission needs of the former to inform the work of the latter. As data owners focus on the creation of valuable data products for the data catalog, users can weigh in with what exactly they’re looking for, enhancing products’ usefulness. This collaborative approach stands in direct contrast to the imposing, “hunt and find” nature of a data swamp.

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Learn how Data Mesh architecture can help your organization meet the standards of the Federal Data Strategy

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2. Domain-driven Data Products

What It Is: At the heart of data mesh is the idea of treating data as a product. This means each data set is carefully curated, maintained, and served by a domain-specific team that understands its context, use cases, and users. By doing so, data products become more relevant, reliable, and accessible to those who need them, transforming data into an asset that drives decision-making and innovation.

The Advantages: Data mesh’s focus on local expertise and autonomy leads to better quality data products that are closely aligned with team objectives. By reducing dependency on central data teams, data mesh enables quicker access to data and faster time to insights while enabling teams to iterate rapidly. It also leads to higher quality analysis because users know exactly what they are getting from a data product, reducing the risk of incorrect assumptions or interpretations that can lead to poor decision making.

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3. Observability and Data Integrity

What It Is: In data mesh architecture, observability — visibility into the operational health of your data infrastructure — becomes an inherent feature at every level of the data ecosystem. Observability equates to the ability to proactively manage and oversee the health, quality, and performance of data pipelines, processes, and systems.

The Advantages: Observability provides a clear, auditable trail of how data is accessed, used, and transformed, improving governance and compliance and building trust in the data and the insights derived from it. Ultimately, observability helps ensure data quality and integrity while also boosting operational efficiency, as clarity around the state of your data infrastructure can help reduce downtime and smooth out processes.

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Maturing Your Data Architecture

Data mesh offers many significant advantages over traditional data architecture and allows you to escape your data swamps before they can negatively impact your productivity, decision making, or regulatory compliance.

However, implementing data mesh comes with its own challenges. Your organization must:

Determine your domains of expertise and assign data product owners who will manage their data products from end to end. There should be a structure in place to resolve any disputes across domains that may arise.

Develop the necessary infrastructure to support a distributed architecture, including data pipelines, storage solutions, and governance mechanisms that can work across various domains. Implementing a centralized data catalog where data products can be easily discovered is critical to making data mesh architecture work.

Cultivate a culture where data is valued as a product, which will require training, incentivizing, or even restructuring teams to embrace this new mindset.

You will also inevitably face issues around standardization, data security, and the complexity of managing multiple systems, all of which should be addressed at the appropriate domain level.

One proven method to navigate these challenges and revolutionize your data architecture is partnering with our experts at ľAV. We are committed to helping federal organizations complete the transformation from traditional data architecture to data mesh, escape their data swamps, and create more efficient, sustainable data ecosystems.

PATRICK ELDER
Director, Data & AI CoE
ANTHONY ZECH
Director, Data & AI
ROSS SERINO
Vice President, Cloud Operations

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]]> Oh, the Possibilities: Balancing Innovation and Risk Around Generative AI /ecs-insight/article/oh-the-possibilities-balancing-innovation-and-risk-around-generative-ai/ Wed, 03 Jan 2024 12:00:38 +0000 http://ecstech.flywheelstaging.com/?p=27562 How does an organization move forward with responsible AI use? By proactively building into its ethics model a focus on governance, risk mitigation, and compliance.

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By
ľAV Vice President of Governance, Risk, and Compliance
and
ľAV Director of Governance, Risk, and Compliance

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For every organization, there is a delicate balance to strike between innovation and risk — one that informs every interaction between your employees, customers, key stakeholders, and the supply chain. We’d be hard-pressed to name a more seismic innovation than the explosion of artificial intelligence (AI), and particularly generative AI (GAI) with its ability to dynamically generate highly realistic, relative outputs. Scaling alongside this innovation are the risks, whether they are preexisting risks such as the proliferation of disinformation or emerging risks such as AI “hallucinations,” the leakage of sensitive information, or inference attacks.

The AI genie is out of the bottle. There’s no going back to the days before large language models (LLMs), such as OpenAI’s ChatGPT and Google’s Bard, broke into mainstream public consciousness. The question we now face is, how do we move forward responsibly? The best answer: proactively building into your organization’s AI ethics model a focus on governance, risk mitigation, and compliance.

Once your organization has determined GAI will add value to the business, you can take concrete steps to ensure ethical GAI use, protect sensitive information, and maintain regulatory compliance.

Building an AI Corporate Responsibility Framework

Taking ownership of how your organization leverages GAI to create value is critical. In so many instances, the worst unintended outcomes of technological innovation can be avoided in the design and planning phases but are incredibly difficult to counteract once products come to market.

GAI is no different. Proactively building an ethos of wise governance into your organization’s AI ethics model can mitigate many of the most harmful risks, from security threats to data leakage to employees not adhering to your established guidelines.

Preventing Security Threats

How exactly does GAI put your organization at increased cyber risk? GAI can reduce barriers of entry for threat actors through enhanced spear phishing, deep fakes, and audio impersonations.

Malicious actors — even novice individuals — can use GAI services to generate phishing campaigns, malware, and other malicious code, despite some GAI services implementing protections against these threats. What steps can your organization take to prevent these security threats? Here’s a short list:

1

Most importantly, revisit cybersecurity best practices around access management, asset management, and detection. Consider deploying AI-driven detection tools capable of identifying and blocking malicious GAI content and activities, as well as AI-powered security solutions that can analyze network traffic and user behavior to detect anomalies and potential threats. Develop an internal testing regime to test your systems and applications for vulnerabilities that could be exploited by GAI. Share information about emerging threats with trusted partners and industry peers.
2

Automate software updates and patching and put data loss prevention controls in place. Such controls could include data anonymization, encryption, and secure storage. Clear consent mechanisms, data minimization, and purpose limitation principles should be employed to protect individual privacy rights and prevent potential data breaches.
3

Develop an “innovation sandbox” to support various parts of the organization and ensure a safe environment for innovation.
4

Understand the “criticality” of GAI to your business. Is it now a critical service for the success and future of your business?
5

Understand the type of information to be provided to a GAI service and ensure this is reflected in data flow mappings.

Transparency and Understanding AI

It’s impossible to fully explain an AI model’s outputs if you don’t have any insight into how it interprets information or makes decisions. Transparency, or the extent to which a user can understand an AI’s inner workings, makes the use of GAI safer and more reliable. Transparency will also help secure employee buy-in regarding your AI ethics framework and explain any guidelines and constraints you put in place.

Here are some areas to focus on as your organization seeks to improve transparency around GAI use, which in turn will fortify your corporate responsibility framework:

By striving for transparency, you can enhance the explainability and interpretability of your AI models, enabling stakeholders, including end users and regulatory bodies, to understand why and how a particular output or decision was reached. It allows for critical evaluation, identification of biases, and identification of potential errors or shortcomings in the system.
Transparency is crucial for assessing and mitigating biases in AI models. Without understanding the underlying mechanisms, it becomes difficult to identify and rectify biases that may be present in the training data or the decision-making algorithms. Ensure the technology builds in robust test and evaluation processes to include human-in-the-loop feedback.
Transparency plays a pivotal role in ensuring ethical and legal compliance in AI applications. As AI increasingly impacts areas such as healthcare, finance, and justice, it is essential to understand how AI models generate actionable information. This understanding enables organizations to ensure compliance with regulatory frameworks, industry standards, and ethical guidelines, thus avoiding potential legal issues and reputational damage.
Transparency empowers users by providing them with insights into how AI models affect their experiences and decisions. Users can make informed choices and exercise control over their interactions with AI systems. Transparency helps users understand how their data is being used, how decisions are made, and the implications of the AI-generated information, enabling them to assert their rights and preferences.
Transparent AI models facilitate continuous improvement and safety enhancements. With transparency, organizations can analyze the behavior of AI systems, identify weaknesses, and address potential risks or biases. This iterative approach to model refinement ensures that AIs evolve to deliver more accurate, reliable, and secure actionable information over time.

Governance, Risk, and Compliance

As GAI becomes more pervasive, compliance with existing regulations and laws becomes increasingly important. Organizations must navigate the complex regulatory landscape to ensure their AI systems adhere to industry-specific guidelines, consumer protection laws, and data privacy regulations like the General Data Protection Regulation (GDPR).

Here are some key steps for fostering innovation while maintaining compliance with legal and ethical requirements:

  1. Include organizational usage of GAI in mapping of organizational compliance requirements. Visibility is key, as is a comprehensive understanding of where and how AI systems are being leveraged within your organization.
  2. Establish an ethical governance committee to support decision-making at a business and technical level. Establish a process for individuals to interact with this committee for new potential uses of GAI/AI. This committee should also review current uses of GAI with a regular cadence to ensure those uses are meeting expectations and support security and compliance postures.
  3. Understand how the usage of AI impacts existing compliance requirements for your organization, e.g., the Equal Credit Opportunity Act (ECOA), the Fair Housing Act (FHA), the Federal Food, Drug, and Cosmetic Act (FD&C Act), and so on.
  4. While AI-specific compliance statutes are lagging far behind the technology’s exponential advance, start thinking now about reporting to boards and governing bodies.
  5. Update your policies and procedures on your organizational stance regarding the use of GAI and understand how that use impacts your organization’s resiliency efforts and business continuity.

Striking the Balance

GAI has the potential to revolutionize various aspects of society, but it is vital to address the governance, risk, and compliance concerns associated with this technology. Ethical implications, privacy protection, bias and fairness, and regulatory compliance must be at the forefront of discussions and decision-making processes. By implementing a robust corporate responsibility framework, your organization can strike the balance between innovation and risk, harnessing the power of GAI while neutralizing the most dangerous risks to your organization.

SHAYLA TREADWELL, PH.D

ľAV Vice President of Governance, Risk, and Compliance

WILLIAM RANKIN

ľAV Director of Governance, Risk, and Compliance

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]]> Charting a New Course With the AI Executive Order /ecs-insight/article/charting-a-new-course-with-the-ai-executive-order/ Thu, 07 Dec 2023 12:00:03 +0000 http://ecstech.flywheelstaging.com/?p=27372 As the groundbreaking executive order regarding AI development and deployment takes effect, ľAV’ Shayla Treadwell explores key takeaways and how organizations can achieve responsible AI use.

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By Ph.D.
ľAV Vice President of Governance, Risk, and Compliance

Navigating a Safer, More Secure, More Trustworthy AI Paradigm

The rapid evolution of AI has transformed industries ranging from healthcare to finance and forced a shift in government priorities. Acknowledging the immense potential and associated risks of AI, the United States government has taken a momentous step. On October 30, 2023, President Biden issued the Executive Order on the .

This executive order (EO) addresses accountability for organizations developing and deploying AI, not only for generative (GAI) systems, but all systems leveraging AI. The EO likely signals a shift in the market, as its prescribed actions will impact all sectors of the AI economy, from start-ups to mature organizations. It will require rigorous evaluation of organizations’ use of AI and the extent to which AI products are leveraged through third parties.

Let’s review eight key principles and priorities any organization developing or deploying AI should take from this groundbreaking EO. Then, learn how ľAV can help guide your organization to more responsible AI use.

For organizations developing and deploying AI systems, we recommend the . Looking into the future, that guidance will likely not end there; however, the EO does provide eight principles and priorities that organizations are expected to follow.

Eight Principles and Priorities From the AI Executive Order

The EO lists eight guiding principles and priorities to advance and govern the development and use of AI:

12345678/8

Safety and Security

Artificial intelligence must be safe and secure. This principle requires a goal to have reliable, repeatable, and standardized evaluations of AI systems, policies, institutions, and other mechanisms to help test and mitigate risks.

Responsible innovation, competition, and collaboration should be promoted. This includes investments in education, training, research and development, and addressing intellectual property rights for small and large businesses.

To ensure responsible AI development and utilization, there’s a commitment to support American workers. This involves providing job training, promoting collective bargaining, and ensuring that AI enhances job quality and worker well-being rather than posing risks or disruptions.

Artificial Intelligence policies should be aligned with efforts to advance equity and civil rights to help promote racial equity and support underserved communities.

The interest of Americans who interact with or purchase AI-enabled products must be protected.

The privacy and civil liberties of Americans must be protected. This includes ensuring that the retention and collection of data uphold privacy standards.

Managing risk from the government’s use of AI and increasing its internal capacity to regulate, govern, and support is critical. This includes taking steps to attract, retain, and develop public service-oriented AI professionals.

The government should lead the way in global societal, economic, and technological processes. This leadership involves working with international partners to develop a framework to manage risk, discover AI potential, and promote a common framework to work through challenges.

Global Collaboration for More Ethical AI

In its concluding sections, the EO emphasizes U.S. leadership in helping to shape global AI endeavors, urging government officials to engage in international cooperation and multi-stakeholder collaboration. The administration underscores the significance of voluntary commitments made by American technology firms, advocating the establishment of a robust international framework for effectively managing AI-related risks and capitalizing on its benefits. The EO mandates the Secretaries of Commerce and State to collaborate with international partners on setting global technical standards, with a stipulated report outlining a plan for global engagement to be delivered within 270 days.

Finally, the EO underscores the government’s commitment to not only harnessing the potential of AI but also to leading the way in addressing its challenges, fostering a collaborative, global environment for the responsible development and deployment of AI technologies.

Achieving Responsible AI Use With ľAV

As the legal landscape around AI continues to take shape, it’s urgent for organizations to prioritize responsible and ethical AI use with an understanding of both the benefits and the potential risks. Even if we understand the goals of the EO, however, it’s not always clear how to turn those goals into action.

ľAV’ experts know how to incorporate governance, risk mitigation, and compliance into your organization’s AI ethics model. With a robust AI corporate responsibility framework in place, your organization will be better positioned to leverage AI ethically, protect sensitive information, and maintain regulatory compliance.

Talk To An Expert

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