AI Mania: DeepSeek Doubling Down on the Evolving, Growing Need for AI

The public launch of the DeepSeek artificial intelligence (AI) chatbot in early 2025 caused a significant media buzz, with it reported as a ChatGPT alternative that had been trained at a far lower cost. The global stock markets took note, especially in terms of its competitors’ positions, and DeepSeek was quickly established as part of the AI revolution.

As an IT professional seeking to stay updated on the rapidly evolving AI technology landscape, you likely want to understand what DeepSeek offers and its potential impact on your IT operations. Does it, for example, hold any real potential to reshape IT service management (ITSM)?

To help, this blog takes a closer look at what DeepSeek is, its aims, and whether the buzz around it is justified.

DeepSeek explained

As with SysAid’s nomenclature, DeepSeek is both an AI company focused on developing large-scale language models (LLMs) optimized for various enterprise and technical use cases and its AI chatbot offering. Unlike OpenAI’s ChatGPT and Google’s Gemini, which have focused on consumer use cases, DeepSeek is positioned in a more technical, developer-centric AI space. For example, code generation, automation, and operational intelligence.

DeepSeek offers models such as DeepSeek-VL (Vision Language) and DeepSeek-Coder. These are not only trained on conversational data but also on large codebases, documentation repositories, and technical instructions. This focus helps to position DeepSeek for developer productivity, IT automation, and operations efficiency use cases. DeepSeek is targeted at practical utility for technical teams, making it especially relevant for IT organizations seeking domain-relevant AI tools.

What caused all the DeepSeek interest?

DeepSeek managed to get attention in a crowded AI market quickly. There were likely many factors involved. Some relate to the technology, but others are external to DeepSeek (the organization). For example, the level of investor attention, with venture capital firms keen to back what was seen as a challenger to OpenAI (and ChatGPT) in the enterprise AI space. DeepSeek’s Chinese origins might have also played a part, especially when pitted against the perceived US AI dominance, and the relative costs of training the AI models (it’s claimed that the DeepSeek V3 model was trained for US$6 million versus the US$100 million cost for OpenAI’s GPT-4).

Returning to the technology aspects of DeepSeek interest, there are three key differentiators:

1. Focused specialization – with DeepSeek going “deep” rather than “broad.” For IT professionals, this facilitates higher performance in niche use cases like infrastructure scripting, log analysis, and data pipeline troubleshooting.
2. Offering open models with strong performance, DeepSeek has released open-source models that outperform many commercial alternatives in benchmark tests.
3. Community growth – developers and data scientists have backed DeepSeek’s tools due to their fine-tuned accuracy and practical relevance. This has driven widespread experimentation and created “grassroots” enthusiasm.

Ultimately, DeepSeek has redefined what AI tools can do in technical environments.

How DeepSeek is impacting the tech industry and ITSM

The tech industry sees DeepSeek as part of a wave of domain-specific AI tools. Tools designed not for “casual conversation” but for solving real-world enterprise problems. However, the perceived benefits of DeepSeek to IT extend further than this. For example, the initial feedback suggested that DeepSeek’s models are faster to deploy, easier to fine-tune, and more effective at understanding complex logic.

While DeepSeek isn’t marketed as an ITSM solution, its capabilities fit well with the needs of IT operations. For example, in:

• Automated troubleshooting scripts – generating or updating scripts that automate resolution tasks
• Log analysis and incident pattern recognition – analyzing visual dashboards or unstructured logs to identify root causes faster
• Knowledge base enrichment – auto-generating knowledge articles that both reduce ticket volumes (via self-help) and assist in IT support.

DeepSeek vs. other AI solutions (such as OpenAI)

How DeepSeek compares to OpenAI and other major players in the AI space is probably best summed up by the term “generalist vs. specialist.” OpenAI’s GPT-4 and related tools are built for versatility and excel in various natural language processing (NLP) tasks, such as writing, summarizing, translation, ideation, and conversation.

Whereas DeepSeek is a specialist AI platform. It focuses heavily on code generation, technical documentation, and infrastructure-level intelligence. Its models, like DeepSeek-Coder, are trained on code repositories and technical materials such that DeepSeek’s models are optimized for understanding and generating code.

So while OpenAI’s GPT is widely used for drafting documentation, building chatbots, automating routine support tasks, and creating internal knowledge bases, DeepSeek’s target is developer tools and integrated development environments (IDEs), automated script generation, log parsing and infrastructure debugging, and contextual support for continuous integration and continuous delivery (CI/CD) pipelines.

Another key difference is DeepSeek’s open architecture. While GPT operates largely as a black-box SaaS API, DeepSeek’s models are open-access, so organizations can fine-tune them locally or integrate them into proprietary environments.

Deepseek and security: Why the Deep-fake and “untrusted vendor” wave matters

Unfortunately, DeepSeek has been shown to amplify familiar attack paths, such as:

• Prompt-attack “x-ray” via Chain-of-Thought. Research findings: DeepSeek-R1 openly emits its internal reasoning in <think> tags. Red teams at Trend Micro and HiddenLayer demonstrated that this transparency enables attackers to steal system prompts, leak sensitive data, and force jailbreaks (HiddenLayer). Secrets such as API keys and credentials become visible, and adversaries learn exactly how to coerce the model into writing malware or bypassing guardrails.
• Data-sovereignty and compliance dragnet. Research findings: Intel 471 notes that DeepSeek, as a Chinese firm, is subject to the 2017 National Intelligence Law. A misconfigured ClickHouse database recently exposed plaintext chat logs and API keys; Taiwan and several EU regulators have already barred DeepSeek on official devices. Feeding sensitive prompts into the public service risks disclosure to both threat actors and foreign state agencies, jeopardising SOC 2, GDPR, and CCPA programs.
• Malware and scam ecosystem around the brand. Research findings: ESET (WeLiveSecurity) and Cisco researchers catalogued fake installers and look-alike domains that drop infostealers and RATs; SEO-poisoning campaigns push “DeepSeek-R1.Leaked.Version.exe.” Employees searching for “free” DeepSeek clients can infect corporate endpoints, creating footholds that sidestep traditional e-mail-centric controls.

There are also generic risks associated with some Chinese technology companies:

• Data Sovereignty and National Security Laws – Under China’s 2017 National Intelligence Law, domestic companies may be compelled to hand over data to the Chinese government, even if it resides on servers abroad.
• Supply-chain risks – Hardware or software from untrusted vendors can include backdoors, spyware, or telemetry components that exfiltrate data.
• Unclear data-handling and privacy practices – Opaque processing raises red flags for GDPR, CCPA, and SOC 2.
• State-sponsored threats – Certain vendors have been linked to APT campaigns targeting global businesses and infrastructure.

How SysAid delivers AI-powered ITSM securely

Security is an important consideration with AI. SysAid’s AI use includes the following security measures:

• Responsible-AI guardrails. What we do: We design and test our solutions according to the OWASP Top 10 for LLM Apps (2025). Our filters remove any model output that exposes Chain-of-Thought, and we ensure that no customer data is used for model retraining. Additionally, all AI calls are logged, signed, and RBAC-scoped. This blocks prompt-injection spill-over and provides forensics-grade traceability.
• Zero-Trust runtime. What we do: Continuous identity and device validation, SentinelOne EDR, Ironscales adaptive phishing defence, and east-west micro-segmentation. Hence, a rogue DeepSeek binary on one laptop can’t pivot into core IT assets.
• Certified hosting and encryption. What we do: Multi-region AWS/Azure pods with ISO 27001, ISO 27017, and SOC 2 Type II attestation; TLS 1.3 in transit, AES-256 at rest, and tenant-specific KMS. This keeps data inside audited jurisdictions and short-circuits vendor-risk questionnaires.
• Third-party and geo-risk vetting. What we do: Every library/SaaS API passes a supplier-risk review, and SysAid avoids AI models hosted in high-risk jurisdictions or subject to China’s intelligence mandate. This removes forced-disclosure exposure and hidden supply-chain implants.
• Continuous assurance. Annual grey-box pen-tests (ISO 27001 and SOC 2 aligned), quarterly vulnerability scans, CSPM/SSPM against CIS Benchmarks, plus dedicated red-team exercises for AI features. This demonstrates real-world resilience, not just paper compliance.
• Security by design. SysAid is SOC 2 Type II, ISO 27001, and ISO 27017 certified with GDPR and CCPA-compliant processing. Data is hosted only in trusted, ISO-certified, region-specific clouds (AWS/Azure).
• Secure AI methodologies. SysAid uses the responsible-AI framework (OWASP Top 10 LLM) with protection against prompt-injection, data-leakage, and model manipulation. There’s RBAC on every AI query, with full audit logging. No customer data is used to train models by default. Plus, there’s proactive monitoring for AI abuse (such as phishing or impersonation).
• Zero-Trust Architecture. SysAid operates continuous authentication and authorisation, with SentinelOne endpoint protection. There’s also Ironscales’ email protection with phishing simulations and user reporting.
• Vendor risk management. SysAid enforces strict third-party evaluations and does not depend on untrusted AI models or infrastructure from high-risk jurisdictions. All AI integrations, cloud providers, and open-source components pass security and privacy requirements.

 Key security takeaways for customers

1. Deep-fake resilience – Filters, identity-verification workflows, and AI-abuse monitoring stop synthetic social-engineering lures before they reach agents.
2. Data-sovereignty certainty – Your data remains in audited regions under EU/US privacy law; there’s no hidden routing through high-risk jurisdictions.
3. Audit-ready from day one – SOC 2, ISO 27001/27017, GDPR, and CCPA controls are baked in, trimming weeks off vendor-risk reviews.
4. Transparent AI – Every Copilot action is explainable, logged, and reversible; you remain the data owner.

“The hype around generative AI and low-cost offshore tooling is real—but so are the threats. SysAid’s stance is simple: innovate, yet never at the expense of customer trust. Our layered controls, certified environments, and responsible-AI guardrails let organizations harness AI-driven ITSM without handing attackers—or foreign governments—the keys to the kingdom.”

– Alexander Raif, Chief Information Security Officer, SysAid.

What ITSM teams need to know about DeepSeek

Notwithstanding the many security risks outlined above, DeepSeek isn’t a plug-and-play option for ITSM tools yet. Instead, it requires some technical integration effort. However, it’s highly adaptable, and unlike rigid SaaS AI platforms, DeepSeek allows your teams to train and tweak models based on their unique environment.

As to whether DeepSeek is a true ITSM game-changer, the jury is still out. It’s certainly more than hype, and when security concerns are mitigated, its focus on technical accuracy, openness, and developer needs makes it one of the most relevant AI offerings for IT teams available today.

To learn more about how other organizations are using AI in ITSM, see here.