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In late August 2024, Cisco agreed to acquire Robust Intelligence, while Check Point agreed to acquire Cyberint. The deals were strategic acquisitions—not conventional venture investments—and the targets addressed different needs: Cisco sought technology for testing and protecting AI models and applications; Check Point added external-risk intelligence, including attack-surface monitoring and brand protection. Neither company disclosed a purchase price. As of August 2026, these are historical deals, not the companies’ latest AI-security moves.
Two acquisitions, two different security layers
The headline’s “AI investments” shorthand can obscure the distinction between the transactions. Cisco’s target, Robust Intelligence, worked on security for AI systems themselves. Cyberint, Check Point’s target, focused on an organization’s external exposure and threat environment. They were adjacent parts of a changing security landscape, not equivalent products.
| Buyer | Target | Primary capability | Strategic layer | Price disclosed? |
|---|---|---|---|---|
| Cisco | Robust Intelligence | AI-model and application validation, adversarial testing, and runtime protection | Security for AI | No |
| Check Point | Cyberint | External-risk intelligence, attack-surface management, dark-web monitoring, and brand protection | Exposure management and threat intelligence | No |
The agreements were announced in the week of August 26–30, 2024. Contemporaneous coverage described both as strategic tuck-ins and reported that terms were not disclosed. They were not transactions comparable in scale with Cisco’s approximately $28 billion purchase of Splunk, as The Associated Press reported.
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Cisco: testing and protecting AI systems
Robust Intelligence developed tools to assess AI-model and application risks. Its capabilities included automated testing—often described as algorithmic red teaming—to probe systems such as large language models for vulnerabilities and potential attack paths. The offering was positioned for both pre-deployment validation and protection at runtime.
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The intended strategic fit was Cisco Security Cloud: adding AI-specific security capabilities to a broader security platform and its network and security data flows. Cisco’s later AI Defense announcement described a product direction for securing enterprise AI, including the Robust Intelligence technology context. That is evidence of a product strategy, not proof that every target capability was immediately or universally integrated after the acquisition announcement.
It helps to distinguish two ideas often bundled under “AI security”:
- AI for security uses AI to help detect threats, investigate alerts, or automate response.
- Security for AI protects models, applications, data flows, prompts, outputs, and the infrastructure or integrations around AI systems.
Robust Intelligence was relevant chiefly to the second category. As organizations put AI services into use, security teams need to understand what systems are being used and how data and requests move through them. Cisco’s network and security footprint offered a potential integration point, but customers still need to verify which controls are available for their deployment, models, and existing products.
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Cyberint’s focus was external risk management and threat intelligence. Its capabilities included attack-surface management, monitoring of deep- and dark-web sources, phishing and impersonation detection, brand protection, supply-chain intelligence, and discovery of fake websites or social-media accounts.
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Those tools can help a security operations center identify threats and exposed assets beyond an organization’s internal network. They do not, by themselves, validate an AI model, prevent prompt injection, or govern an AI agent’s permissions. Cyberint therefore complemented Check Point’s platform and SOC ambitions from a different direction than Robust Intelligence did for Cisco.
The distinction matters when evaluating vendor claims: discovering an exposed domain or leaked credential is not the same control as testing an AI application, and neither automatically remediates the risk found. Buyers should ask how findings are prioritized, who owns remediation, and how alerts reach the workflows their teams actually use.
Why buy specialized capabilities?
Enterprise AI adoption was moving faster than many security programs, which were designed around conventional applications and infrastructure. AI systems can bring risks such as prompt injection, model manipulation, data leakage, jailbreaks, and insecure third-party integrations. At the same time, organizations’ internet-facing assets, cloud services, APIs, SaaS tools, and external identities make the perimeter harder to define.
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Acquiring specialist technology and expertise can be faster than building a comparable capability from scratch, and a large vendor may be able to connect a specialized tool to an existing platform. That integration is a strategic rationale, not a guarantee: acquired products can take time to converge with a buyer’s platform, and standalone depth may differ from what is eventually bundled or generally available.
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In that sense, Cisco was buying an AI assurance and protection layer, while Check Point was adding an external-exposure and threat-intelligence layer. Both addressed risks associated with a more connected, AI-heavy enterprise, but they did not solve the same problem.
What was—and was not—announced
At the time, the confirmed facts were that Cisco had agreed to acquire Robust Intelligence and Check Point had agreed to acquire Cyberint; the financial terms were not disclosed, and the transactions were subject to customary closing conditions. Cisco had previously invested in Robust Intelligence and was also a customer, according to the original coverage. Check Point’s announcement came shortly after Nadav Zafrir was named incoming CEO, with Gil Shwed moving to executive-chairman duties.
Those announcements did not establish an exact deal value, immediate integration, revenue contribution, or measurable reduction in customer risk. Nor did Check Point’s Cyberint acquisition mean it had bought a foundation-model company. Avoid treating analyst estimates as disclosed prices or assuming that an announcement means a product is already available in every region or edition.
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Later activity shows that both companies continued expanding security capabilities around AI, but it should not be conflated with the 2024 transactions.
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Check Point’s later AI-security direction included Lakera, which focuses on protecting generative-AI applications and models against risks such as prompt injection, data leakage, and model manipulation. Check Point’s 2025 annual filing says the Lakera acquisition was completed on October 22, 2025, and that Check Point acquired Cyata in February 2026 to address discovery, understanding, and governance of autonomous AI agents. Check Point’s Lakera announcement describes the rationale for that separate deal. Together, the later moves span external exposure, AI-application protection, and agent security.
Cisco’s subsequent corporate-development activity has included Galileo, for AI-system observability; Astrix Security, for non-human identity and credential security; and WideField Security, for identity telemetry relevant to agent, machine, and human identities in Splunk’s agentic SOC strategy. Cisco lists these activities in its acquisition record and corporate-development materials. Separately, Cisco joined the AI Infrastructure Partnership in May 2025, an initiative that initially sought to unlock $30 billion in capital and potentially mobilize up to $100 billion including debt financing for data centers and enabling infrastructure. That is an infrastructure-investment effort, not part of the Robust Intelligence acquisition; see Cisco’s announcement.
What enterprise buyers should take from the deals
Start with the security problem, not the vendor’s broad use of “AI.” An organization looking to validate models before deployment or protect AI applications at runtime has a different requirement from one seeking discovery of exposed internet assets, dark-web monitoring, or impersonation detection. Agent identity and access control, data-loss prevention, and AI-assisted SOC operations are further distinct needs.
- For AI-model and application security: ask whether controls cover pre-deployment testing, runtime protection, private and third-party models, self-hosted workloads, and developer workflows. Check data-residency, logging, and model-privacy terms.
- For external-risk management: ask what assets and sources are monitored, whether cloud assets, domains, certificates, leaked credentials, and supplier infrastructure are in scope, and how findings are prioritized and routed for remediation.
- For either platform: confirm current product availability, licensing, integration requirements, regional coverage, and whether a capability is a standalone product, platform feature, or managed service. Do not infer availability from an acquisition announcement.
Platform integration can reduce tool sprawl, particularly for organizations already using Cisco or Check Point products, but may increase vendor concentration. A specialist tool can offer more depth in one area while creating another console and integration burden. A network-centric product may have useful traffic visibility but not necessarily the deepest coverage of model-development workflows. External-risk findings can be noisy without good prioritization, and identifying an exposure does not ensure it is fixed.
The central lesson is to map each capability to a specific control layer. The 2024 Cisco deal concerned security for AI systems; the Check Point deal strengthened external-risk and threat-intelligence coverage. Neither transaction, on its own, demonstrated that a company had secured every part of the AI-era attack surface.
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