Latest news with #PrashantPrahlad
Yahoo
4 hours ago
- Business
- Yahoo
Datadog (DDOG) Launches Powerful New Tools to Secure AI Workloads
Datadog, Inc. (NASDAQ:) is one of the . On June 10, the company unveiled new capabilities for detecting and remediating critical security risks across customers' AI environments. Extending investment to secure its customers' cloud and AI applications, the need for new capabilities has arisen out of AI workloads fostering new attack surfaces. Microservices now come with capabilities that can spin up autonomous agents for revealing secrets, shipping code, and even calling external APIs without any human intervention. To avoid developers who rely on third-party code repositories being exposed to poisoned code and hidden vulnerabilities, the company has introduced Datadog Code Security. Now generally available, it empowers developers and security teams to detect and prioritize vulnerabilities in first-party code and open source libraries, leveraging AI to solve complex issues in both AI and traditional applications. Another capability unveiled by the company is Datadog LLM Observability. Now generally available, the LLM Observability monitors AI models and looks for harmful behavior across prompts and responses within an organization's AI applications. The company has also introduced Bits AI Security Analyst, a new AI agent that automatically triages security signals and performs detailed investigations of potential threats. Lastly, Datadog's Workload Protection will allow customers to monitor the interaction between LLMs and their host environment, allowing production AI models to stay secure. 'AI has exponentially increased the ever-expanding backlog of security risks and vulnerabilities organizations deal with. This is because AI-native apps are not deterministic; they're more of a black box and have an increased surface area that leaves them open to vulnerabilities like prompt or code injection. The latest additions to Datadog's Security Platform provide preventative and responsive measures—powered by continuous runtime visibility—to strengthen the security posture of AI workloads, from development to production.' -Prashant Prahlad, VP of Products, Security at Datadog. Datadog, Inc. (NASDAQ:DDOG) offers a cloud-based SaaS platform for monitoring and analytics, specializing in cloud computing and AI-powered cybersecurity products. While we acknowledge the potential of DDOG as an investment, we believe certain AI stocks offer greater upside potential and carry less downside risk. If you're looking for an extremely undervalued AI stock that also stands to benefit significantly from Trump-era tariffs and the onshoring trend, see our free report on the best short-term AI stock. READ NEXT: and Disclosure: None. Sign in to access your portfolio
Scoop
2 days ago
- Business
- Scoop
Datadog Broadens AI Security Features To Counter Critical Threats
Press Release – Datadog Launch of Code Security and new security capabilities strengthen posture across the AI stack, from data and AI models to applications. AUCKLAND – JUNE 11, 2025 – Datadog, Inc. (NASDAQ: DDOG), the monitoring and security platform for cloud applications, today announced new capabilities to detect and remediate critical security risks across customers' AI environments —from development to production—as the company further invests to secure its customers' cloud and AI applications. AI has created a new security frontier in which organisations need to rethink existing threat models as AI workloads foster new attack surfaces. Every microservice can now spin up autonomous agents that can mint secrets, ship code and call external APIs without any human intervention. This means one mistake could trigger a cascading breach across the entire tech stack. The latest innovations to Datadog's Security Platform, presented at DASH, aim to deliver a comprehensive solution to secure agentic AI workloads. 'AI has exponentially increased the ever-expanding backlog of security risks and vulnerabilities organisations deal with. This is because AI-native apps are not deterministic; they're more of a black box and have an increased surface area that leaves them open to vulnerabilities like prompt or code injection,' said Prashant Prahlad, VP of Products, Security at Datadog. 'The latest additions to Datadog's Security Platform provide preventative and responsive measures—powered by continuous runtime visibility—to strengthen the security posture of AI workloads, from development to production.' Securing AI Development Developers increasingly rely on third-party code repositories which expose them to poisoned code and hidden vulnerabilities, including those that stem from AI or LLM models, that are difficult to detect with traditional static analysis tools. To address this problem, Datadog Code Security, now Generally Available, empowers developer and security teams to detect and prioritise vulnerabilities in their custom code and open-source libraries, and uses AI to drive remediation of complex issues in both AI and traditional applications—from development to production. It also prioritises risks based on runtime threat activity and business impact, empowering teams to focus on what matters most. Deep integrations with developer tools, such as IDEs and GitHub, allow developers to remediate vulnerabilities without disrupting development pipelines. Hardening Security Posture of AI Applications AI-native applications act autonomously in non-deterministic ways, which makes them inherently vulnerable to new types of attacks that attempt to alter their behaviour such as prompt injection. To mitigate these threats, organisations need stronger security controls—such as separation of privileges, authorisation bounds, and data classification across their AI applications and the underlying infrastructure. Datadog LLM Observability, now Generally Available, monitors the integrity of AI models and performs toxicity checks that look for harmful behavior across prompts and responses within an organisation's AI applications. In addition, with Datadog Cloud Security, organisations are able to meet AI security standards such as the NIST AI framework out-of-the-box. Cloud Security detects and remediates risks such as misconfigurations, unpatched vulnerabilities, and unauthorised access to data, apps, and infrastructure. And with Sensitive Data Scanner (SDS), organisations can prevent sensitive data—such as personally identifiable information (PII)—from leaking into LLM training or inference data-sets, with support for AWS S3 and RDS instances now available in Preview. Securing AI at Runtime The evolving complexity of AI applications is making it even harder for security analysts to triage alerts, recognise threats from noise and respond on-time. AI apps are particularly vulnerable to unbound consumption attacks that lead to system degradation or substantial economic losses. The Bits AI Security Analyst, a new AI agent integrated directly into Datadog Cloud SIEM, autonomously triages security signals—starting with those generated by AWS CloudTrail—and performs in-depth investigations of potential threats. It provides context-rich, actionable recommendations to help teams mitigate risks more quickly and accurately. It also helps organisations save time and costs by providing preliminary investigations and guiding Security Operations Centers to focus on the threats that truly matter. Finally, Datadog's Workload Protection helps customers continuously monitor the interaction between LLMs and their host environment. With new LLM Isolation capabilities, available in preview, it detects and blocks the exploitation of vulnerabilities, and enforces guardrails to keep production AI models secure. To learn more about Datadog's latest AI Security capabilities, please visit: Code Security, new tools in Cloud Security, Sensitive Data Scanner, Cloud SIEM, Workload and App Protection, as well as new security capabilities in LLM Observability were announced during the keynote at DASH, Datadog's annual conference. The replay of the keynote is available here. During DASH, Datadog also announced launches in AI Observability, Applied AI, Log Management and released its Internal Developer Portal.
Scoop
2 days ago
- Business
- Scoop
Datadog Broadens AI Security Features To Counter Critical Threats
AUCKLAND – JUNE 11, 2025 – Datadog, Inc. (NASDAQ: DDOG), the monitoring and security platform for cloud applications, today announced new capabilities to detect and remediate critical security risks across customers' AI environments —from development to production—as the company further invests to secure its customers' cloud and AI applications. AI has created a new security frontier in which organisations need to rethink existing threat models as AI workloads foster new attack surfaces. Every microservice can now spin up autonomous agents that can mint secrets, ship code and call external APIs without any human intervention. This means one mistake could trigger a cascading breach across the entire tech stack. The latest innovations to Datadog's Security Platform, presented at DASH, aim to deliver a comprehensive solution to secure agentic AI workloads. 'AI has exponentially increased the ever-expanding backlog of security risks and vulnerabilities organisations deal with. This is because AI-native apps are not deterministic; they're more of a black box and have an increased surface area that leaves them open to vulnerabilities like prompt or code injection,' said Prashant Prahlad, VP of Products, Security at Datadog. 'The latest additions to Datadog's Security Platform provide preventative and responsive measures—powered by continuous runtime visibility—to strengthen the security posture of AI workloads, from development to production.' Securing AI Development Developers increasingly rely on third-party code repositories which expose them to poisoned code and hidden vulnerabilities, including those that stem from AI or LLM models, that are difficult to detect with traditional static analysis tools. To address this problem, Datadog Code Security, now Generally Available, empowers developer and security teams to detect and prioritise vulnerabilities in their custom code and open-source libraries, and uses AI to drive remediation of complex issues in both AI and traditional applications—from development to production. It also prioritises risks based on runtime threat activity and business impact, empowering teams to focus on what matters most. Deep integrations with developer tools, such as IDEs and GitHub, allow developers to remediate vulnerabilities without disrupting development pipelines. Hardening Security Posture of AI Applications AI-native applications act autonomously in non-deterministic ways, which makes them inherently vulnerable to new types of attacks that attempt to alter their behaviour such as prompt injection. To mitigate these threats, organisations need stronger security controls—such as separation of privileges, authorisation bounds, and data classification across their AI applications and the underlying infrastructure. Datadog LLM Observability, now Generally Available, monitors the integrity of AI models and performs toxicity checks that look for harmful behavior across prompts and responses within an organisation's AI applications. In addition, with Datadog Cloud Security, organisations are able to meet AI security standards such as the NIST AI framework out-of-the-box. Cloud Security detects and remediates risks such as misconfigurations, unpatched vulnerabilities, and unauthorised access to data, apps, and infrastructure. And with Sensitive Data Scanner (SDS), organisations can prevent sensitive data—such as personally identifiable information (PII)—from leaking into LLM training or inference data-sets, with support for AWS S3 and RDS instances now available in Preview. Securing AI at Runtime The evolving complexity of AI applications is making it even harder for security analysts to triage alerts, recognise threats from noise and respond on-time. AI apps are particularly vulnerable to unbound consumption attacks that lead to system degradation or substantial economic losses. The Bits AI Security Analyst, a new AI agent integrated directly into Datadog Cloud SIEM, autonomously triages security signals—starting with those generated by AWS CloudTrail—and performs in-depth investigations of potential threats. It provides context-rich, actionable recommendations to help teams mitigate risks more quickly and accurately. It also helps organisations save time and costs by providing preliminary investigations and guiding Security Operations Centers to focus on the threats that truly matter. Finally, Datadog's Workload Protection helps customers continuously monitor the interaction between LLMs and their host environment. With new LLM Isolation capabilities, available in preview, it detects and blocks the exploitation of vulnerabilities, and enforces guardrails to keep production AI models secure. To learn more about Datadog's latest AI Security capabilities, please visit: Code Security, new tools in Cloud Security, Sensitive Data Scanner, Cloud SIEM, Workload and App Protection, as well as new security capabilities in LLM Observability were announced during the keynote at DASH, Datadog's annual conference. The replay of the keynote is available here. During DASH, Datadog also announced launches in AI Observability, Applied AI, Log Management and released its Internal Developer Portal.
Techday NZ
2 days ago
- Business
- Techday NZ
Datadog unveils AI-powered security tools for cloud & code
Datadog has introduced a suite of artificial intelligence security tools designed to detect and mitigate risks across cloud and AI environments. New AI agent The company has launched Bits AI Security Analyst, an AI agent that autonomously investigates potential threats and supports teams in managing risks with greater efficiency and accuracy. Integrated into Datadog Cloud SIEM, this agent triages security signals—starting with those generated by AWS CloudTrail—and performs detailed investigations into possible threats. Actionable, context-driven recommendations are then provided to help security teams respond more swiftly. "AI has exponentially increased the ever-expanding backlog of security risks and vulnerabilities organizations deal with. This is because AI-native apps are not deterministic; they're more of a black box and have an increased surface area that leaves them open to vulnerabilities like prompt or code injection," said Prashant Prahlad, Vice President of Products, Security at Datadog. "The latest additions to Datadog's Security Platform provide preventative and responsive measures—powered by continuous runtime visibility—to strengthen the security posture of AI workloads, from development to production." Enhancing code security Datadog Code Security, now generally available, aims to help developers and security teams detect and prioritise vulnerabilities not just in proprietary code but also within open-source libraries. The platform is specifically designed to uncover issues that may be present in large language model (LLM) integrations and AI-powered code, as these can be difficult to identify using traditional static analysis tools. The solution also uses artificial intelligence to facilitate the remediation of complex problems and ranks risks based on runtime activity and business impact. Deep integrations with widely-used developer environments, including integrated development environments (IDEs) and GitHub, are intended to allow faster remediation workflows without interrupting established development processes. Strengthening AI application security With AI-native applications operating autonomously and often in unpredictable ways, new types of attacks such as prompt injection have become more prevalent. Datadog's updated security offerings include features to help organisations implement stronger security controls through measures such as separation of privileges, finely-tuned authorisation, and data classification throughout their AI application landscape and infrastructure. Datadog LLM Observability, now also generally available, monitors the integrity of AI models, with tools to identify harmful or toxic behaviours across prompts and responses in enterprise AI applications. Other updates to Datadog Cloud Security support compliance with standards such as the NIST AI framework. This suite can uncover and remediate misconfigurations, unpatched vulnerabilities, and instances of unauthorised data or infrastructure access. The Sensitive Data Scanner, now supporting AWS S3 and RDS instances in preview, helps prevent personal or sensitive information from inadvertently being incorporated in LLM training data or inference processes. Monitoring runtime risks The complexity of AI-based applications increases the challenge for security analysts to manage alerts, distinguish credible threats from benign signals, and respond in a timely manner. According to Datadog, AI applications are at particular risk of attacks that could lead to resource exhaustion or financial damage if not detected early. Bits AI Security Analyst is designed to reduce the workload on Security Operations Centres by providing initial investigations and filtering for more relevant threats. The new solution aims to enable teams to act on rich context and prioritised guidance so they can focus resources where they matter most. Additional updates include Datadog Workload Protection, which now features LLM Isolation capabilities in preview. This enables continuous monitoring of interactions between LLMs and their host environments, helping to detect and prevent exploitation of vulnerabilities while enforcing controls to protect production AI models. Datadog's new security features encompass Code Security, updated Cloud Security tools, Sensitive Data Scanner, Cloud SIEM, Workload and Application Protection, and expanded abilities within LLM Observability. These updates are designed to give organisations multiple layers of risk mitigation as they increasingly deploy AI in critical workflows.
Associated Press
2 days ago
- Business
- Associated Press
Datadog Expands AI Security Capabilities to Enable Comprehensive Protection from Critical AI Risks
Launch of Code Security and new security capabilities strengthen posture across the AI stack, from data and AI models to applications New York, New York--(Newsfile Corp. - June 10, 2025) - Datadog, Inc. (NASDAQ: DDOG), the monitoring and security platform for cloud applications, today announced new capabilities to detect and remediate critical security risks across customers' AI environments -from development to production-as the company further invests to secure its customers' cloud and AI applications. AI has created a new security frontier in which organizations need to rethink existing threat models as AI workloads foster new attack surfaces. Every microservice can now spin up autonomous agents that can mint secrets, ship code and call external APIs without any human intervention. This means one mistake could trigger a cascading breach across the entire tech stack. The latest innovations to Datadog's Security Platform, presented at DASH, aim to deliver a comprehensive solution to secure agentic AI workloads. 'AI has exponentially increased the ever-expanding backlog of security risks and vulnerabilities organizations deal with. This is because AI-native apps are not deterministic; they're more of a black box and have an increased surface area that leaves them open to vulnerabilities like prompt or code injection,' said Prashant Prahlad, VP of Products, Security at Datadog. 'The latest additions to Datadog's Security Platform provide preventative and responsive measures-powered by continuous runtime visibility-to strengthen the security posture of AI workloads, from development to production.' Securing AI Development Developers increasingly rely on third-party code repositories which expose them to poisoned code and hidden vulnerabilities, including those that stem from AI or LLM models, that are difficult to detect with traditional static analysis tools. To address this problem, Datadog Code Security, now Generally Available, empowers developer and security teams to detect and prioritize vulnerabilities in their custom code and open-source libraries, and uses AI to drive remediation of complex issues in both AI and traditional applications-from development to production. It also prioritizes risks based on runtime threat activity and business impact, empowering teams to focus on what matters most. Deep integrations with developer tools, such as IDEs and GitHub, allow developers to remediate vulnerabilities without disrupting development pipelines. Hardening Security Posture of AI Applications AI-native applications act autonomously in non-deterministic ways, which makes them inherently vulnerable to new types of attacks that attempt to alter their behavior such as prompt injection. To mitigate these threats, organizations need stronger security controls-such as separation of privileges, authorization bounds, and data classification across their AI applications and the underlying infrastructure. Datadog LLM Observability, now Generally Available, monitors the integrity of AI models and performs toxicity checks that look for harmful behavior across prompts and responses within an organization's AI applications. In addition, with Datadog Cloud Security, organizations are able to meet AI security standards such as the NIST AI framework out-of-the-box. Cloud Security detects and remediates risks such as misconfigurations, unpatched vulnerabilities, and unauthorized access to data, apps, and infrastructure. And with Sensitive Data Scanner (SDS), organizations can prevent sensitive data-such as personally identifiable information (PII)-from leaking into LLM training or inference data-sets, with support for AWS S3 and RDS instances now available in Preview. Securing AI at Runtime The evolving complexity of AI applications is making it even harder for security analysts to triage alerts, recognize threats from noise and respond on-time. AI apps are particularly vulnerable to unbound consumption attacks that lead to system degradation or substantial economic losses. The Bits AI Security Analyst, a new AI agent integrated directly into Datadog Cloud SIEM, autonomously triages security signals-starting with those generated by AWS CloudTrail-and performs in-depth investigations of potential threats. It provides context-rich, actionable recommendations to help teams mitigate risks more quickly and accurately. It also helps organizations save time and costs by providing preliminary investigations and guiding Security Operations Centers to focus on the threats that truly matter. Finally, Datadog's Workload Protection helps customers continuously monitor the interaction between LLMs and their host environment. With new LLM Isolation capabilities, available in preview, it detects and blocks the exploitation of vulnerabilities, and enforces guardrails to keep production AI models secure. To learn more about Datadog's latest AI Security capabilities, please visit: Code Security, new tools in Cloud Security, Sensitive Data Scanner, Cloud SIEM, Workload and App Protection, as well as new security capabilities in LLM Observability were announced during the keynote at DASH, Datadog's annual conference. The replay of the keynote is available here. During DASH, Datadog also announced launches in AI Observability, Applied AI, Log Management and released its Internal Developer Portal. About Datadog Datadog is the observability and security platform for cloud applications. Our SaaS platform integrates and automates infrastructure monitoring, application performance monitoring, log management, user experience monitoring, cloud security and many other capabilities to provide unified, real-time observability and security for our customers' entire technology stack. Datadog is used by organizations of all sizes and across a wide range of industries to enable digital transformation and cloud migration, drive collaboration among development, operations, security and business teams, accelerate time to market for applications, reduce time to problem resolution, secure applications and infrastructure, understand user behavior and track key business metrics. Forward-Looking Statements This press release may include certain 'forward-looking statements' within the meaning of Section 27A of the Securities Act of 1933, as amended, or the Securities Act, and Section 21E of the Securities Exchange Act of 1934, as amended including statements on the benefits of new products and features. These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies and prospects, which are based on the information currently available to us and on assumptions we have made. Actual results may differ materially from those described in the forward-looking statements and are subject to a variety of assumptions, uncertainties, risks and factors that are beyond our control, including those risks detailed under the caption 'Risk Factors' and elsewhere in our Securities and Exchange Commission filings and reports, including the Annual Report on Form 10-K filed with the Securities and Exchange Commission on May 6, 2025, as well as future filings and reports by us. Except as required by law, we undertake no duty or obligation to update any forward-looking statements contained in this release as a result of new information, future events, changes in expectations or otherwise. Contact Dan Haggerty [email protected] To view the source version of this press release, please visit
