Technology

IBM Introduces Granite 3.0: High Performing AI Models Built for Business

Published

2 years ago

October 21, 2024

New Granite 3.0 8B & 2B models, released under the permissive Apache 2.0 license, show strong performance across many academic and enterprise benchmarks, able to outperform or match similar-sized modelsNew Granite Guardian 3.0 models deliver IBM’s most comprehensive guardrail capabilities to advance safe and trustworthy AINew Granite 3.0 Mixture-of-Experts models enable extremely efficient inference and low latency, suitable for CPU-based deployments and edge computingNew Granite Time Series model achieved state-of-the-art performance in zero/few-shot forecasting, outperforming models 10 times largerIBM unveils next generation of Granite-powered watsonx Code Assistant for general purpose coding; Debuts new tools in watsonx.ai for building and deploying AI applications and agentsAnnounces Granite will become the default model of Consulting Advantage, an AI-powered delivery platform used by IBM’s 160,000 consultants to bring new solutions to clients faster

ARMONK, N.Y., Oct. 21, 2024 /PRNewswire/ — Today, at IBM’s (NYSE: IBM) annual TechXchange event the company announced the release of its most advanced family of AI models to date, Granite 3.0. IBM’s third-generation Granite flagship language models can outperform or match similarly sized models from leading model providers on many academic and industry benchmarks, showcasing strong performance, transparency and safety.

Consistent with the company’s commitment to open-source AI, the Granite models are released under the permissive Apache 2.0 license, making them unique in the combination of performance, flexibility and autonomy they provide to enterprise clients and the community at large.

IBM’s Granite 3.0 family includes:

General Purpose/Language: Granite 3.0 8B Instruct, Granite 3.0 2B Instruct, Granite 3.0 8B Base, Granite 3.0 2B BaseGuardrails & Safety: Granite Guardian 3.0 8B, Granite Guardian 3.0 2BMixture-of-Experts: Granite 3.0 3B-A800M Instruct, Granite 3.0 1B-A400M Instruct, Granite 3.0 3B-A800M Base, Granite 3.0 1B-A400M Base

The new Granite 3.0 8B and 2B language models are designed as ‘workhorse’ models for enterprise AI, delivering strong performance for tasks such as Retrieval Augmented Geneneration (RAG), classification, summarization, entity extraction, and tool use. These compact, versatile models are designed to be fine-tuned with enterprise data and seamlessly integrated across diverse business environments or workflows.

While many large language models (LLMs) are trained on publicly available data, a vast majority of enterprise data remains untapped. By combining a small Granite model with enterprise data, especially using the revolutionary alignment technique InstructLab – introduced by IBM and RedHat in May – IBM believes businesses can achieve task-specific performance that rivals larger models at a fraction of the cost (based on an observed range of 3x-23x less cost than large frontier models in several early proofs-of-concept1).

The Granite 3.0 release reaffirms IBM’s commitment to building transparency, safety, and trust in AI products. The Granite 3.0 technical report and responsible use guide provide a description of the datasets used to train these models, details of the filtering, cleansing, and curation steps applied, along with comprehensive results of model performance across major academic and enterprise benchmarks.

Critically, IBM provides an IP indemnity for all Granite models on watsonx.ai so enterprise clients can be more confident in merging their data with the models.

Raising the bar: Granite 3.0 benchmarks

The Granite 3.0 language models also demonstrate promising results on raw performance.

On standard academic benchmarks defined by Hugging Face’s OpenLLM Leaderboard, the Granite 3.0 8B Instruct model’s overall performance leads on average against state-of-the-art-performance of similar-sized open source models from Meta and Mistral. On IBM’s state-of-the-art AttaQ safety benchmark, the Granite 3.0 8B Instruct model leads across all measured safety dimensions compared to models from Meta and Mistral.2

Across the core enterprise tasks of RAG, tool use, and tasks in the Cybersecurity domain, the Granite 3.0 8B Instruct model shows leading performance on average compared to similar-sized open source models from Mistral and Meta.3

The Granite 3.0 models were trained on over 12 trillion tokens on data taken from 12 different natural languages and 116 different programming languages, using a novel two-stage training method, leveraging results from several thousand experiments designed to optimize data quality, data selection, and training parameters. By the end of the year, the 3.0 8B and 2B language models are expected to include support for an extended 128K context window and multi-modal document understanding capabilities.

Demonstrating an excellent balance of performance and inference cost, IBM offers its Granite Mixture of Experts (MoE) Architecture models, Granite 3.0 1B-A400M and Granite 3.0 3B-A800M, as smaller, lightweight models that could be deployed for low latency applications as well as CPU-based deployments.

IBM is also announcing an updated release of its pre-trained Granite Time Series models, the first versions of which were released earlier this year. These new models are trained on 3 times more data and deliver strong performance on all three major time series benchmarks, outperforming 10 times larger models from Google, Alibaba, and others. The updated models also provide greater modeling flexibility with support for external variables and rolling forecasts.4

Introducing Granite Guardian 3.0: ushering the next era of responsible AI

As part of this release, IBM is also introducing a new family of Granite Guardian models that permit application developers to implement safety guardrails by checking user prompts and LLM responses for a variety of risks. The Granite Guardian 3.0 8B and 2B models provide the most comprehensive set of risk and harm detection capabilities available in the market today.

In addition to harm dimensions such as social bias, hate, toxicity, profanity, violence, jailbreaking and more, these models also provide a range of unique RAG-specific checks such as groundedness, context relevance, and answer relevance. In extensive testing across 19 safety and RAG benchmarks, the Granite Guardian 3.0 8B model has higher overall accuracy on harm detection on average than all three generations of Llama Guard models from Meta. It also showed on par overall performance in hallucination detection on average with specialized hallucination detection models WeCheck and MiniCheck.5

While the Granite Guardian models are derived from the corresponding Granite language models, they can be used to implement guardrails alongside any open or proprietary AI models.

Availability of Granite 3.0 models

The entire suite of Granite 3.0 models and the updated time series models are available for download on HuggingFace under the permissive Apache 2.0 license. The instruct variants of the new Granite 3.0 8B and 2B language models and the Granite Guardian 3.0 8B and 2Bmodels are available today for commercial use on IBM’s watsonx platform. A selection of the Granite 3.0 models will also be available as NVIDIA NIM microservices and through Google Cloud’s Vertex AI Model Garden integrations with HuggingFace.

To help provide developer choice and ease of use and support local, edge deployments, a curated set of the Granite 3.0 models are also available on Ollama and Replicate.

The latest generation of Granite models expand IBM’s robust open-source catalog of powerful LLMs. IBM has collaborated with ecosystem partners like AWS, Docker, Domo, Qualcomm Technologies, Inc. via its Qualcomm® AI Hub, Salesforce, SAP, and others to integrate a variety of Granite models into these partners’ offerings or make Granite models available on their platforms, offering greater choice to enterprises across the world.

Assistants to Agents: realizing the future for enterprise AI

IBM is advancing enterprise AI through a spectrum of technologies – from models and assistants, to the tools needed to tune and deploy AI specifically for companies’ unique data and use-cases. IBM is also paving the way for future AI agents that can self-direct, reflect, and perform complex tasks in dynamic business environments.

IBM continues to evolve its portfolio of AI assistant technologies – from watsonx Orchestrate to help companies build their own assistants via low-code tooling and automation, to a wide set of pre-built assistants for specific tasks and domains such as customer service, human resources, sales, and marketing. Organizations around the world have used watsonx Assistant to help them build AI assistants for tasks like answering routine questions from customers or employees, modernizing their mainframes and legacy IT applications, helping students explore potential career paths, or providing digital mortgage support for home buyers.

Today IBM also unveiled the upcoming release of the next generation of watsonx Code Assistant, powered by Granite code models, to offer general-purpose coding assistance across languages like C, C++, Go, Java, and Python, with advanced application modernization capabilities for Enterprise Java Applications.6 Granite’s code capabilities are also now accessible through a Visual Studio Code extension, IBM Granite.Code.

IBM also plans to release new tools to help developers build, customize and deploy AI more efficiently via watsonx.ai – including agentic frameworks, integrations with existing environments and low-code automations for common use-cases like RAG and agents.7

IBM is focused on developing AI agent technologies which are capable of greater autonomy, sophisticated reasoning and multi-step problem solving. The initial release of the Granite 3.0 8B model features support for key agentic capabilities, such as advanced reasoning and a highly-structured chat template and prompting style for implementing tool use workflows. IBM also plans to introduce a new AI agent chat feature to IBM watsonx Orchestrate, which uses agentic capabilities to orchestrate AI Assistants, skills, and automations that help users increase productivity across their teams.8 IBM plans to continue building agent capabilities across its portfolio in 2025, including pre-built agents for specific domains and use-cases.

Expanded AI-powered delivery platform to supercharge IBM consultants with AI

IBM is also announcing a major expansion of its AI-powered delivery platform, IBM Consulting Advantage. The multi-model platform contains AI agents, applications, and methods like repeatable frameworks that can empower 160,000 IBM consultants to deliver better and faster client value at a lower cost.

As part of the expansion, Granite 3.0 language models will become the default model in Consulting Advantage. Leveraging Granite’s performance and efficiency, IBM Consulting will be able to help maximize the return-on-investment for the generative AI projects of IBM clients.

Another key part of the expansion is the introduction of IBM Consulting Advantage for Cloud Transformation and Management and IBM Consulting Advantage for Business Operations. Each includes domain-specific AI agents, applications, and methods infused with IBM’s best practices so IBM consultants can help accelerate client cloud and AI transformations in tasks, like code modernization and quality engineering, or transform and execute operations across domains, like finance, HR and procurement.

To learn more about Granite and IBM’s AI for Business strategy, visit https://www.ibm.com/granite.

1 Cost calculations are based on API cost per million tokens pricing of IBM watsonx for open models and openAI for GPT4 models (assuming blend of 80% inout, 20% output) for customer proofs-of-concept.
2 IBM Research technical paper: Granite 3.0 Language Models
3 IBM Research technical paper: Granite 3.0 Language Models
4 The Tiny Time Mixer: Fast Pre-Trained Models for Enhanced Zero/Few Shot Forecasting on Multivariate Time Series
5 Evaluation results published in Granite Guardian GitHub Repo
6 Planned availability for Q4 2024
7 Planned availability for Q4 2024
8 Planned availability for Q1 2025

Media Contact:
Amy Angelini
alangeli@us.ibm.com

SOURCE IBM

Technology

Govee Strengthens Its Commitment to User Security with Updated Security Response Center

Published

5 minutes ago

November 12, 2026

BELLEVUE, Wash., Nov. 12, 2026 /PRNewswire/ — Govee, a global leader in smart lighting, today announced the update of its official Security Response Center, reinforcing its ongoing commitment to protecting user privacy and product security through greater transparency and closer collaboration with the global security research community.

At Govee, user privacy and product security are top priorities. The updated Security Response Center provides a centralized platform where users and researchers can access security advisories, stay informed about security updates, and learn more about Govee’s coordinated vulnerability disclosure process.

As part of this initiative, Govee is strengthening its engagement with security researchers worldwide through its Vulnerability Reward Program. Researchers are encouraged to responsibly report potential vulnerabilities, with eligible submissions qualifying for bounty rewards following review and verification, directly through Govee’s Security Response Center Portal.

The platform also features a Contributors section, publicly recognizing researchers who have helped improve Govee products and services, with their consent.

Through continued investment in security, transparent communication, and collaboration with independent researchers, Govee remains committed to delivering safer, more reliable products and experiences for its users.

For more information, visit the Govee Security Response Center: security.govee.com

About Govee

Govee has been revolutionizing the smart living experience with innovative, ambient lighting solutions since 2017. From living spaces, gaming setups, and outdoor areas, Govee’s smart home tech is not just visually stunning, but also functional – transforming small everyday moments into more personalized and brighter engaging lighting experiences. Embracing the idea that users should “Life is Colorful” and push the boundaries of what lighting can do by blending design, and utility. To learn more about Govee, please visit govee.com.

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SOURCE Govee

Technology

Rafay and Fortanix Help Neoclouds, Sovereign AI Clouds and Enterprises Deliver Confidential AI as a Service

Published

5 minutes ago

November 12, 2026

Joint solution lets operators run customers’ AI workloads in attested, hardware-isolated environments where sensitive data and models stay encrypted even while in use.

SUNNYVALE, Calif., Nov. 12, 2026 /PRNewswire/ — Rafay Systems and Fortanix® Inc. today announced a joint solution that lets neoclouds, sovereign AI clouds and enterprises deliver confidential AI as a Service. The Rafay Platform now orchestrates workloads that run on CNCF® Confidential Containers (CoCo) with Fortanix Confidential AI, extending the companies’ existing integration for confidential virtual machines.

With the joint solution, operators can offer AI workloads that run inside hardware-based Trusted Execution Environments (TEEs), where encryption keys are released only after Fortanix verifies the environment and AI workload through cryptographic attestation. End users launch those workloads from the Rafay Developer Hub without filing a support ticket.

In regulated industries such as financial services, healthcare and the public sector, many of the mission critical AI use cases involve sensitive data such as customer records, patient information and financial transactions, and model providers need to protect the proprietary models that process it. These workloads often run on GPU infrastructure operated by a 3rd party: a neocloud, a sovereign cloud or a shared internal AI factory. Encryption protects data at rest and in transit, but without confidential computing, data and model weights must be decrypted for processing, leaving them exposed in CPU and GPU memory to anyone with privileged access to the infrastructure. Research from Fortanix and Atomik Research found that while 93% of AI leaders understand traditional encryption does not protect data while AI is processing it, only half can fully verify that protection is in place during inference.

“Confidential computing enables model owners to deploy proprietary AI models on third-party infrastructure with reduced risk of exposing valuable intellectual property, while creating new revenue opportunities for cloud operators to offer premium, security-focused AI inference services.” said Mohan Atreya, chief product officer at Rafay. “Fortanix verifies the environment through cryptographic attestation and controls the secure key release. Rafay turns that into a governed, multi-tenant service, so a neocloud or sovereign cloud can offer confidential AI as a SKU, and its customers can run sensitive workloads on infrastructure they don’t operate. With Confidential Containers, that model now reaches the Kubernetes pod, where AI teams already ship their workloads.”

How the Joint Solution Works

CoCo, a CNCF Incubating project, applies confidential computing to the Kubernetes pod. Built on Kata Containers, CoCo runs each pod in its own lightweight confidential virtual machine, with memory encrypted by the CPU and, on supported hardware, GPUs included in the trust boundary. Developers deploy workloads with standard Kubernetes workflows.

Fortanix Confidential Computing Manager (CCM) performs composite attestation across the confidential computing environment, verifying the integrity of CPU and GPU trusted execution environments, firmware, and software components before authorizing the release of encryption keys. Fortanix Data Security Manager (DSM), backed by a FIPS 140-3 Level 3 HSM certification, releases encryption keys only to environments CCM has attested. If attestation fails, encryption keys remain protected, preventing the workload from accessing sensitive data or executing successfully.

The Rafay Platform turns that infrastructure into a service operators can deliver to their own customers:

Cluster automation: Rafay blueprints install the Fortanix CoCo add-ons and keep them in sync on every confidential cluster.Multi-tenancy: Operators onboard GPU infrastructure once, then give each customer an isolated tenant.Self-service SKUs: Administrators package a confidential workload as a SKU in Rafay SKU Studio, and end users launch it on demand from the Rafay Developer Hub.Governance: Policy, access controls and quotas apply across tenants and clusters.

“Every AI Factory faces a trust dilemma. Model owners want their weights hidden from operators and customers; enterprises want their data hidden from model owners and operators; and operators must prove they can see neither, even via another tenant or compromised software,” said Anuj Jaiswal, Chief Product Officer at Fortanix. “Fortanix and Rafay resolve that dilemma in one click. Rafay orchestrates the deployment of proprietary models and AI applications across on-premises, sovereign and cloud infrastructures. Fortanix Confidential AI releases keys only to attested workloads, so model owners, enterprises, and operators, each keep control of what they own, every time it runs.”

Use Cases

Neoclouds and GPU cloud providers can offer confidential AI as a premium service, backed by attestation evidence that tenant workloads run in TEEs and that keys go only to verified workloads. Sovereign AI clouds can keep keys in an HSM under the operator’s control, on-premises or as SaaS, and attest what is running before any data is decrypted. Regulated enterprises in financial services, healthcare and the public sector can run AI on customer, patient or financial data while keeping it protected from the infrastructure operator during processing. AI model providers can deliver proprietary models into customer environments with weights that stay encrypted until the environment is attested.

A recorded demo shows the end-to-end workflow, with a customer of a neocloud launching a confidential application via the Rafay end user portal with attestation and key release by Fortanix.

Availability

Rafay support for CoCo-based deployments with Fortanix Confidential AI is available now, alongside the existing confidential VM integration. Fortanix announced Fortanix Confidential AI for CoCo on Oct. 19, 2026.

About Rafay Systems

Rafay Systems is a leading software provider powering the operators building the AI cloud, including neoclouds, telecommunications providers, enterprises, and sovereign AI operators. The Rafay Platform lets these organizations operationalize GPU and compute infrastructure with self-service automation, governance, and multi-tenancy, spanning bare metal provisioning, infrastructure lifecycle management, virtual machines, Kubernetes, GPU PaaS, AI development environments, and Token Factory for publishing AI models as token-metered inference services. By simplifying orchestration and operations across this stack, Rafay helps operators increase GPU utilization and turn raw infrastructure into monetizable, production-ready AI services, all while maintaining security, consistency, and control. Rafay is a validated partner in the NVIDIA AI Cloud Ready initiative. For more information, visit rafay.co.

About Fortanix

Fortanix is the global leader in data and AI security and a pioneer of Confidential Computing, delivering a unified platform to protect sensitive data, AI models, and applications across on-premises and multi-cloud environments—at rest, in transit, and in use. Built on hardware-enforced security, Fortanix enables workloads to run in tamper-proof, isolated enclaves, protecting against data leakage, model extraction, and unauthorized access, even from privileged insiders.

As enterprises scale modern AI factories, Fortanix provides a Confidential AI foundation built on attestation-based trust, secure key release, and zero-trust enforcement. This ensures AI systems remain verifiable, auditable, and trustworthy—while enabling crypto agility and long-term resilience through post-quantum cryptography (PQC).

Trusted by leading enterprises and sovereign entities in highly regulated industries and powered by an industry-standard approach to Confidential Computing supported by leading technology providers, including Intel, Microsoft, and NVIDIA, Fortanix enables organizations to safely use sensitive data and drive AI adoption with confidence.

Media Contacts

Rafay Systems: Angela Shugarts, angela@rafay.co

Fortanix: BOCA Marketing for Fortanix, Fortanix@bocamarketing.com

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SOURCE Rafay Systems

Technology

Allot Announces Third Quarter 2026 Financial Results

Published

5 minutes ago

November 12, 2026

Revenue growth of 16% and SECaaS ARR increases 41% year-over-year

HOD HASHARON, Israel, Nov. 12, 2026 /PRNewswire/ — Allot Ltd. (NASDAQ: ALLT) (TASE: ALLT), a leading global provider of innovative Security-as-a-Service (SECaaS) and network intelligence solutions for communications service providers and enterprises, today announced its unaudited financial results for the third quarter of 2026.

Financial Highlights for the Third Quarter of 2026

Strong revenue growth to $30.6 million, up 16% year-over-year;Security-as-a-Service (SECaaS) revenues of $9.9 million, increasing 37% year-over-year;September 2026 SECaaS ARR* of $38.8 million, up 41% year-over-year;GAAP operating income of $2.8 million, compared with $2.2 million in the third quarter of 2025;Non-GAAP operating income of $3.9 million compared with $3.7 million in the third quarter of 2025;Operating cash flow of $7.4 million, up 85% compared with $4.0 million in the third quarter of 2025.

Management Comment

Eyal Harari, CEO of Allot, commented, ” This quarter demonstrated continued excellent execution and showed the strength of our business model. Allot is now a sustainably profitable company, and we continue to expand our profitability as we grow our business. We delivered a strong third quarter, combining double-digit revenue growth with improved profitability and robust cash generation. Growth was driven by both SECaaS and SMART solutions, that are both now showing double-digit year-over-year revenue increases. The SMART product growth follows our high level of backlog which we are converting into revenue, following our recent significant wins.”

Mr. Harari added, “SECaaS ARR continues to grow strongly at 41% year-over-year, further building our recurring revenue base and our visibility into the quarters ahead. We continue to innovate to find new ways to protect consumers, as demonstrated with our recent launch of Allot IOT security. As threat actors use AI to target individuals and businesses at-scale, it is becoming more critical for service providers to provide a layer of protection for their end-customers, supporting demand for cyber-security solutions.”

Mr. Harari concluded, “Given the momentum, we are raising the midpoint of our guidance for 2026 and narrowing the range to between $116 million and $118 million. We also expect SECaaS revenue growth for 2026, to be above 40%.”

Third Quarter 2026 Financial Results Summary

Total revenues for the third quarter of 2026 were $30.6 million, a 16% increase year-over-year compared with $26.4 million in the third quarter of 2025.

Gross profit on a GAAP basis for the third quarter of 2026 was $21.9 million (gross margin of 71.6%), a 16% increase compared with $18.9 million (gross margin of 71.4%) in the third quarter of 2025.

Gross profit on a non-GAAP basis for the third quarter of 2026 was $22.0 million (gross margin of 71.9%), a 15% increase compared with $19.1 million (gross margin of 72.2%) in the third quarter of 2025.

Operating income on a GAAP basis for the third quarter of 2026 was $2.8 million (operating margin of 9.2%), a 31% increase compared with $2.2 million (operating margin of 8.1%) in the third quarter of 2025.

Operating income on a non-GAAP basis for the third quarter of 2026 was $3.9 million (operating margin of 12.9%), a 6% increase compared with $3.7 million (operating margin of 14.0%) in the third quarter of 2025.

Net income on a GAAP basis for the third quarter of 2026 was $3.4 million, or $0.07 per diluted share, compared with $2.8 million, or $0.07 per diluted share, in the third quarter of 2025.

Net income on a non-GAAP basis for the third quarter of 2026 was $4.7 million, or $0.09 per diluted share, compared with $4.6 million, or $0.10 per diluted share, in the third quarter of 2025.

Operating cash flow generated in the quarter was $7.4 million, compared with $4.0 million in the third quarter of 2025.

Cash and cash equivalents, bank deposits, restricted deposits and investments as of September 30, 2026, totaled $112 million, compared with $88 million as of December 31, 2025.

Conference Call & Webcast:

The Allot management team will host a conference call to discuss its third quarter 2026 earnings results today, November 12, 2026, at 8:30 am ET, 1:30 pm UK, 3:30 pm Israel time. To access the conference call, please dial one of the following numbers:

US: 1-888-668-9141, UK: 0-800-917-5108, Israel: +972-3-918-0644

A live webcast and, following the end of the call, an archive of the conference call, will be accessible on the Allot website at: https://investors.allot.com/

About Allot

Allot Ltd. (NASDAQ: ALLT, TASE: ALLT) is a leading provider of innovative converged cybersecurity solutions and network intelligence offerings for service providers and enterprises worldwide. Allot enhances value to its customers’ customers through its solutions, which are deployed globally for network-native cybersecurity services, network and application analytics, traffic control and shaping, and more. Allot’s multi-service platforms are deployed by over 500 mobile, fixed and cloud service providers and over 1,000 enterprises. Allot’s industry-leading network-native security-as-a-service solution is already used by many millions of subscribers globally.

For more information, visit www.allot.com

Performance Metrics

* SECaaS ARR – measures the current annual recurring SECaaS revenues, calculated as estimated SECaaS revenues for the month of September 2026, multiplied by 12.

GAAP to Non-GAAP Reconciliation:

The Company presents non–GAAP financial measures that adjust GAAP results to exclude items that management considers not reflective of the Company’s ongoing operational performance. Non-GAAP gross profit is defined as GAAP gross profit excluding share-based compensation expenses, amortization of intangible assets and acquisition–related expenses. Non-GAAP operating income is defined as GAAP operating income excluding primarily share-based compensation expenses, amortization of intangible assets and acquisition–related expenses. Non-GAAP net income is defined as GAAP net income excluding primarily share-based compensation expenses, amortization of intangible assets, loss from extinguishment, acquisition–related and other non–recurring expenses, financial income or expenses related to exchange rate differences and changes in tax-related items.

These non-GAAP measures should be considered in addition to, and not as a substitute for, comparable GAAP measures. The non-GAAP results and a full reconciliation between GAAP and non-GAAP results are presented below. The Company provides these non-GAAP financial measures because it believes they present a better measure of the Company’s core business and management uses the non-GAAP measures internally to evaluate the Company’s ongoing performance. Accordingly, the Company believes they are useful to investors in enhancing an understanding of the Company’s operating performance.

Safe Harbor Statement

This release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. All statements contained in this release that do not relate to matters of historical fact should be considered forward-looking statements, including, without limitation, statements regarding our expected financial performance and operational performance including our revenue guidance, revenue, growth, demand for our solutions, our ability to execute our strategy, our ability to convert backlog into revenue, our product innovation, capital allocation, share repurchase programs, maximizing shareholder value, and future opportunities, as well as statements that include the words “expect,” “intend,” “plan,” “believe,” “project,” “forecast,” “estimate,” “may,” “should,” “anticipate” and similar statements of a future or forward-looking nature. These forward-looking statements express the current beliefs and expectations of Company management. Such statements involve a number of known and unknown risks and uncertainties that could cause our future results, performance or achievements to differ significantly from the results, performance or achievements set forth in such forward-looking statements. Important factors that could cause or contribute to such differences include risks relating to: our accounts receivable, including our ability to collect outstanding accounts and assess their collectability on a quarterly basis; our ability to meet expectations with respect to our financial guidance and outlook; our ability to compete successfully with other companies offering competing technologies; the loss of one or more significant customers; consolidation of, and strategic alliances by, our competitors; government regulation; the timing of completion of key project milestones which impact the timing of our revenue recognition; lower demand for key value-added services; our ability to keep pace with advances in technology and to add new features and value-added services; managing lengthy sales cycles; operational risks associated with large projects; our dependence on third party channel partners for a material portion of our revenues; and other factors discussed under the heading “Risk Factors” in the Company’s annual report on Form 20-F for the fiscal year 2025, filed with the Securities and Exchange Commission as such factors may be updated from time to time in our other filings with the SEC, which are accessible on the SEC’s website at www.sec.gov. Accordingly, you should not rely upon forward-looking statements as predictions of future events. Additionally, the forward-looking statements are made only as of the date hereof, and the Company undertakes no obligation to update or revise the forward-looking statements, whether as a result of new information, future events or otherwise.

Investor Relations Contact:
EK Global Investor Relations
Ehud Helft
+1 212 378 8040
allot@ekgir.com

Public Relations Contact:
Seth Greenberg, Allot Ltd.
+972 54 922 2294
sgreenberg@allot.com

ALLOT LTD.

AND ITS SUBSIDIARIES

CONSOLIDATED BALANCE SHEETS

(U.S. dollars in thousands)

September 30,

December 31,

2026

2025

ASSETS

CURRENT ASSETS:

Cash and cash equivalents

$ 29,650

$ 17,107

Restricted deposit

4,243

3,573

Short-term bank deposits

24,500

15,100

Available-for-sale marketable securities

52,355

48,663

Trade receivables, net (net of allowance for credit losses of $9,095 and
$9,611 on September 30, 2026, and December 31, 2025, respectively)

28,974

17,451

Other receivables and prepaid expenses

6,845

9,906

Inventories

17,011

13,180

Total current assets

163,578

124,980

NON-CURRENT ASSETS:

Severance pay fund

$ 324

$ 295

Restricted deposit

908

3,327

Operating lease right-of-use assets

6,143

5,518

Other assets

835

732

Property and equipment, net

4,978

6,014

Goodwill

31,833

Total non-current assets

45,021

47,719

Total assets

$ 208,599

$ 172,699

LIABILITIES AND

SHAREHOLDERS’ EQUITY

CURRENT LIABILITIES:

Trade payables

$ 2,776

$ 938

Employees and payroll accruals

11,433

9,254

Deferred revenues

42,980

24,700

Short-term operating lease liabilities

1,622

348

Other payables and accrued expenses

14,806

11,919

Total current liabilities

73,617

47,159

LONG-TERM LIABILITIES:

Deferred revenues

$ 9,004

$ 5,912

Long-term operating lease liabilities

4,463

5,392

Accrued severance pay

672

886

Total long-term liabilities

14,139

12,190

SHAREHOLDERS’ EQUITY

120,843

113,350

Total liabilities and shareholders’ equity

$ 208,599

$ 172,699

ALLOT LTD.

AND ITS SUBSIDIARIES

CONSOLIDATED STATEMENTS OF OPERATIONS

(U.S. dollars in thousands, except share and per share data)

Three Months Ended

Nine Months Ended

September 30,

2026

2025

2026

2025

Revenues

$ 30,588

$ 26,405

$ 84,750

$ 73,606

Cost of revenues

8,690

7,539

24,342

21,362

Gross profit

21,898

18,866

60,408

52,244

Operating expenses:

Research and development
costs, net

6,351

5,489

19,624

18,741

Sales and marketing

8,580

8,148

24,445

22,747

General and administrative

4,153

3,079

10,898

9,722

Total operating expenses

19,084

16,716

54,967

51,210

Operating income

2,814

2,150

5,441

1,034

Loss from extinguishment

–

(1,410)

Other income

–

100

Financial income, net

1,091

869

3,851

1,902

Income before tax

3,905

3,019

9,292

1,626

Income tax expenses

486

195

1,358

823

Net income

$ 3,419

$ 2,824

$ 7,934

$ 803

Income per share

Basic

$ 0.07

$ 0.16

$ 0.02

Diluted

$ 0.07

$ 0.16

$ 0.02

Weighted average shares
outstanding

Basic

49,276,707

41,487,057

49,070,116

42,580,555

Diluted

49,806,002

43,253,509

49,845,813

44,909,810

ALLOT LTD.

AND ITS SUBSIDIARIES

CONSOLIDATED STATEMENTS OF CASH FLOWS

(U.S. dollars in thousands)

Three Months Ended

Nine Months Ended

September 30,

2026

2025

2026

2025

Cash flows from operating activities:

Net income

$ 3,419

$ 2,824

$ 7,934

$ 803

Adjustments to reconcile net income to net cash
used in operating activities:

Depreciation and amortization

588

835

1,933

3,254

Share-based compensation

1,123

1,559

3,860

3,989

Capital loss

113

–

113

255

Loss from extinguishment

–

1,410

Other income

–

(100)

Amortization of premium, discount and accrued
interest on marketable securities

162

(237)

(283)

(1,099)

Financial income from lease modification

–

(1,158)

–

Loss (gain) on foreign exchange on cash and
cash equivalents

(44)

(454)

Changes in operating assets and liabilities:

Increase (decrease) in accrued severance pay, net

(243)

117

Decrease (increase) in other assets, other
receivables and prepaid expenses

1,443

(3,817)

510

(2,198)

(Increase) decrease in operating leases liability

(834)

190

(253)

(13)

Increase in operating lease right-of-use asset

404

292

1,131

871

Increase in trade receivables

(3,804)

(679)

(11,523)

(4,332)

Decrease (increase) in inventories

486

(5,703)

(3,831)

(5,597)

Increase in trade payables

1,358

6,274

1,838

6,252

Increase (decrease) in employees and
payroll accruals

2,651

844

2,179

1,417

(Decrease) increase in deferred revenues

(1,963)

420

21,372

2,956

Increase in other payables and accrued expenses

2,125

1,211

2,737

2,126

Net cash provided by operating activities

7,375

3,997

26,415

9,657

Cash flows from investing activities:

(Increase) decrease in restricted deposit

(848)

–

1,749

353

Investment in short-term bank deposits

(13,000)

(16,800)

(44,400)

(32,550)

Withdrawal of short-term bank deposits

19,600

11,050

35,000

31,000

Purchase of property and equipment

(360)

(100)

(1,010)

(789)

Investment in marketable securities

(30,844)

(44,213)

(65,398)

(99,647)

Proceeds from redemption or sale of
marketable securities

35,660

38,694

61,910

88,377

Proceeds from sale of patent

–

100

Net cash provided by (used in)
investing activities

10,208

(11,369)

(12,149)

(13,156)

Cash flows from financing activities:

Issuance of share capital

–

4,617

–

42,308

Purchase of treasury stocks

(1,624)

Exercise of employee stock options

–

238

Redemption of convertible debt

–

(31,410)

Net cash (used in) provided by
financing activities

(1,624)

4,617

(1,624)

11,136

Effect of exchange rate changes on cash and
cash equivalents

(68)

(99)

454

Increase (decrease) in cash and
cash equivalents

15,891

(2,711)

12,543

8,090

Cash and cash equivalents at the beginning
of the period

13,759

26,943

17,107

16,142

Cash and cash equivalents at the end
of the period

$ 29,650

$ 24,232

$ 29,650

$ 24,232

Non-cash activities:

Right-of-use assets obtained in exchange for
operating lease liabilities

–

(12)

593

(83)

Redemption of convertible debt

–

(10,000)

ALLOT LTD.

AND ITS SUBSIDIARIES

RECONCILIATION OF GAAP TO NON-GAAP CONSOLIDATED STATEMENTS OF OPERATIONS

(U.S. dollars in thousands, except per share data)

Three Months Ended

Nine Months Ended

September 30,

2026

2025

2026

2025

(Unaudited)

GAAP cost of revenues

$ 8,690

$ 7,539

$ 24,342

$ 21,362

Share-based compensation

(80)

(211)

(320)

(465)

Amortization of intangible assets

–

(305)

Non-GAAP cost of revenues

$ 8,610

$ 7,328

$ 24,022

$ 20,592

GAAP gross profit

$ 21,898

$ 18,866

$ 60,408

$ 52,244

Share-based compensation

211

320

465

Amortization of intangible assets

–

305

Non-GAAP gross profit

$ 21,978

$ 19,077

$ 60,728

$ 53,014

GAAP operating expenses

$ 19,084

$ 16,716

$ 54,967

$ 51,210

Share-based compensation – Research and development costs, net

(203)

(400)

(731)

(1,022)

Share-based compensation – Sales and marketing

(427)

(466)

(1,590)

(1,237)

Share-based compensation – General and administrative

(413)

(482)

(1,219)

(1,265)

Non-GAAP operating expenses

$ 18,041

$ 15,368

$ 51,427

$ 47,686

GAAP operating Income

$ 2,814

$ 2,150

$ 5,441

$ 1,034

Share-based compensation

1,123

1,559

3,860

3,989

Amortization of intangible assets

$ –

$ 305

Non-GAAP operating Income

$ 3,937

$ 3,709

$ 9,301

$ 5,328

GAAP Net income

$ 3,419

$ 2,824

$ 7,934

$ 803

Share-based compensation

1,123

1,559

3,860

3,989

Amortization of intangible assets

–

305

Loss from extinguishment

–

1,410

Exchange rate differences*

(189)

116

(86)

159

Changes in tax related items

329

627

150

Non-GAAP Net income

$ 4,682

$ 4,579

$ 12,335

$ 6,816

Non-GAAP income per share

Basic

$ 0.10

$ 0.11

$ 0.25

$ 0.16

Diluted

$ 0.09

$ 0.10

$ 0.24

$ 0.15

Weighted average shares outstanding

Basic

49,276,707

41,487,057

49,070,116

42,580,555

Diluted

51,204,432

44,592,351

51,102,519

46,409,437

* Financial income or expenses related to exchange rate differences in connection
with revaluation of assets and liabilities in non-dollar denominated currencies.

Other financial metrics (Unaudited)

U.S. dollars in millions, except top 10 customers as a % of revenues and number of shares

Q3-26

FY 2025

FY 2024

Revenues geographic breakdown

Americas

5.8

19 %

19.1

19 %

14.2